Systems and methods to seed a search

ABSTRACT

Systems and methods to seed a search are described. The system identifies a seed listing included in listings that describe items being offered for sale on a network-based marketplace. The system identifies a seed filter context based on the seed listing and probabilities. The probabilities describe occurrences of attribute-value pairs in a plurality of listings that respectively describe items that were previously transacted on the network-based marketplace. The system extracts values from the seed listing based on the seed filter context. The system initializes the seed filter context based on the values and generates search results based on the seed filter context. The search results include a second plurality of listings that are identified from the first plurality of listings. Finally, the system communicates interface information to a client machine including the seed filter context and at least one listing from the second plurality of listings.

RELATED APPLICATIONS

This application is a continuation-in-part application that claims thepriority benefits of U.S. application Ser. No. 14/460,728, filed Aug.15, 2014 and the priority benefits of U.S. application Ser. No.14/460,690, filed Aug. 15, 2014 and the priority benefits of U.S.application Ser. No. 14/460,767, filed Aug. 15, 2014 all of which claimthe priority benefits of U.S. Provisional Application No. 62/009,817,filed Jun. 9, 2014 which is incorporated in its entirety by reference.

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent files or records, but otherwise reserves all copyrightrights whatsoever. The following notice applies to the software and dataas described below and in the drawings that form a part of thisdocument: Copyright eBay, Inc. 2014, All Rights Reserved.

BRIEF DESCRIPTION OF DRAWINGS

Various ones of the appended drawings merely illustrate exampleembodiments of the present invention and cannot be considered aslimiting its scope.

FIG. 1A illustrates a system to identify a filter set in a querycomprised of keywords, according to an embodiment;

FIG. 1B illustrates a system to identify and present filters, accordingto an embodiment;

FIG. 1C illustrates a system to identify values for a filter that isselected, according to an embodiment;

FIG. 1D illustrates a schematic of a generation of a popularity table,according to an embodiment;

FIG. 1E illustrates a system to seed a search, according to anembodiment;

FIGS. 2A-2D illustrate user interfaces, according to an embodiment;

FIGS. 3A-3C illustrate user interfaces, according to an embodiment;

FIGS. 4A-4B illustrate user interfaces, according to an embodiment;

FIGS. 4C-4D illustrate user interfaces, according to an embodiment;

FIG. 5 illustrates an environment in which example embodiments may beimplemented, according to an embodiment;

FIG. 6A illustrates search metadata, according to an embodiment;

FIG. 6B illustrates domain information, according to an embodiment;

FIG. 6C illustrates classification rule information, according to anembodiment;

FIG. 6D illustrates a classification rule, according to an embodiment;

FIG. 6E illustrates an attribute-value pair, according to an embodiment;

FIG. 6F illustrates an items table, according to an embodiment;

FIG. 6G illustrates a listing, according to an embodiment;

FIG. 6H illustrates structured information, according to an embodiment;

FIG. 6I illustrates a popularity table, according to an embodiment;

FIG. 6J illustrates attribute-value popularity information, according toan embodiment;

FIG. 6K illustrates an image table, according to an embodiment;

FIG. 7 illustrates a method to extract completed listings and generate apopularity table, according to an embodiment;

FIG. 8A illustrates a method to identify a filter set in a querycomprised of keywords;

FIG. 8B illustrates a method to extract filter sets from a keywordquery, according to an embodiment;

FIG. 8C illustrates a method to analyze a query, according to anembodiment;

FIG. 8D illustrates a method to score filter sets, according to anembodiment;

FIG. 9A illustrates a method to identify and present filters, accordingto an embodiment;

FIG. 9B illustrates a method to identify an order of filters in a filtercontext, according to an embodiment;

FIG. 9C illustrates a method to identify filters in a filter proposaland their order of presentation, according to an embodiment;

FIG. 9D illustrates Tables 1-2, according to an embodiment;

FIG. 9E illustrates a method to identify an ordered set of values for afilter name, according to an embodiment;

FIG. 9F illustrates Tables 3-4, according to an embodiment;

FIG. 9G illustrates Table 5, according to an embodiment;

FIG. 10A illustrates a method to identify values for a selected filter,according to an embodiment;

FIG. 10B illustrates a method to generate an interface, according to anembodiment;

FIG. 10C illustrates a method to seed a search, according to anembodiment;

FIG. 10D illustrates a method to identify filters in a seed filtercontext, according to an embodiment;

FIG. 10E illustrates a method to identify filters in a seed filterproposal, according to an embodiment;

FIG. 10F illustrates Table 6, according to an embodiment;

FIG. 10G illustrates Table 7, according to an embodiment;

FIG. 10H illustrates Table 8, according to an embodiment;

FIG. 10I illustrates Table 9, according to an embodiment;

FIG. 11 is a block diagram illustrating an example embodiment of ahigh-level client-server-based network architecture;

FIG. 12 is a block diagram illustrating an example embodiment of apublication system;

FIG. 13 is a block diagram illustrating tables that are utilized by thepublication system, according to an embodiment; and

FIG. 14 is a block diagram of a machine in an example form of acomputing system within which a set of instructions for causing themachine to perform any one or more of the methodologies discussed hereinmay be executed.

DETAILED DESCRIPTION

The description that follows includes systems, methods, techniques,instruction sequences, and computing machine program products thatembody illustrative embodiments of the present invention. In thefollowing description, for purposes of explanation, numerous specificdetails are set forth in order to provide an understanding of anembodiment of the inventive subject matter. It will be evident, however,to those skilled in the art that embodiments of the inventive subjectmatter may be practiced without these specific details. In general,well-known instruction instances, protocols, structures, and techniqueshave not been shown in detail.

FIG. 1A illustrates a first aspect of the present disclosure in the formof a system 10 to identify a filter set in a query comprised ofkeywords, according to an embodiment. The system 10 may receive a query,identify multiple filter sets in the query based on the keywords in thequery, score each filter set, identify a filter set with a highestscore, and utilize the filter set with the highest score to identifylistings (e.g., search results) that describe items for sale on anetwork-based marketplace. A filter set may be comprised of filters thatare further comprised of attribute-value pairs. The system 10 mayidentify one filter set from another based on probabilities thatdescribe occurrences of attribute-value pairs in listings that describepreviously transacted items on a network-based marketplace. Accordingly,the system 10 may select from competing interpretations of the querybased on the popularity of attribute-value pairs in previouslytransacted listings. Broadly, consider a seller who lists an item forsale on a network-based marketplace and a buyer who enters a query thatis received by the network-based marketplace and processed to returnsearch results. For example, FIG. 1A illustrates an operation “A,” wherea seller, who is operating a client machine, may enter informationdescribing an item that is communicated over a network to thenetwork-based marketplace. At operation “B,” the network-basedmarketplace may receive and store the information in a listing in anitems table. For example, the network-based marketplace may receive andstore a title “RED IPHONE FOR SALE—CHEAP” in a listing in the itemstable. At operation “C,” the network-based marketplace may further applyclassification rules to the title to, at operation “D,” generatestructured information in the form of attribute-value pairs. Forexample, the network-based marketplace may apply classification rules tokeywords that comprise the title in the listing to identify theattribute-value pairs, COLOR=RED, BRAND=APPLE, and TYPE=CELL PHONE. Thenetwork-based marketplace may structure keywords in the title toidentify different meanings. Consider, in another example, the word“apple” does not signify a brand, but rather, a fruit. That is, the word“apple” in the title of a listing (e.g., APPLE ORCHARD FORSALE—EXPENSIVE BUT HIGHLY DESIREABLE) may have a different meaning(e.g., FRUIT=APPLE). Also, consider that the network-based marketplacemay identify multiple filter sets for the same keywords in a query.

Returning to FIG. 1A, at operation “E,” a buyer may enter the query “REDAPPLE IPHONE” that, in turn, is received by the network-basedmarketplace that, in turn, applies the classification rules to the queryto identify four filter sets at operation “F.” At operation “G,” thenetwork-based marketplace may identify one filter set from the fourfilter sets for display to the seller as an interpreted meaning of thequery “RED APPLE IPHONE.” The system 10 may identify one filter set frommultiple possible sets by scoring each of the filter sets based onprobabilities that describe occurrences of attribute-value pairs inlistings that describe items previously transacted on a network-basedmarketplace (e.g., completed listings), as described further below. Thatis, each filter set may be scored based on probabilities of each filter,as an attribute value-pair, occurring in the title of completed listingsand based on probabilities of each filter, as an attribute-value pair,co-occurring in the completed listings with each of the other filters inthe filter set. Finally, at operation “G,” the network-based marketplacemay filter the listings in the items table based on a highest scoringfilter set to generate search results for communication to the clientmachine that is operated by the seller. Accordingly, the system 10 mayselect a most popular filter set from a set of filter sets based on theoccurrence of attribute-value pairs in listings that were previouslytransacted on the network-based marketplace.

FIG. 1B illustrates a second aspect of the present disclosure in theform of a system 20 to identify and present filters, according to anembodiment. Broadly, consider a buyer who, at operation “A,” enters thequery “RED APPLE IPHONE” that may be communicated over a network andreceived by a network-based marketplace. At operation “B,” thenetwork-based marketplace may apply the classification rules to thequery to generate, at operation “C,” a filter context in the form of aset of filters “COLOR=RED,” “BRAND=APPLE,” and “TYPE=IPHONE.” Atoperation “D,” the network-based marketplace may identify an order ofpresentation (e.g., “TYPE=IPHONE,” “BRAND=APPLE,” and “COLOR=RED”) forthe filters on a user interface. Further, at operation “E,” the system20 may identify and present a filter proposal in the form of a secondset of filters, including their order for presentation on a userinterface. The system 20 may identify the filters in the filter proposaland their order of presentation based on the filters in the filtercontext and probabilities that describe occurrences of attribute-valuepairs (e.g., filters) in listings that describe items previouslytransacted on the network-based marketplace (e.g., completed listings).That is, each filter in the filter proposal and its order ofpresentation may be determined based on a probability of the filter, asan attribute value-pair, occurring in the completed listings and onprobabilities of the filter, as an attribute-value pair, co-occurring inthe completed listings with each of the other filters in the filtercontext. Finally, the system 20 may generate a user interface includingsearch results identified based on the filter context and communicatethe user interface, over the network, to the client machine.

FIG. 1C illustrates a third aspect of the present disclosure in the formof system 30 to identify values for a filter that is selected, accordingto an embodiment. Broadly, consider a buyer who, at operation “A,”selects the filter “COLOR=BLACK” to update the filter “COLOR=RED” in aconcept query (not shown) “COLOR=RED,” “BRAND=APPLE,” “TYPE=IPHONE.” Theselection may be communicated over a network and received by thenetwork-based marketplace. At operation “B” the network-basedmarketplace may identify a set of filters based on the filter name“COLOR” and the other filters in the concept query (e.g., “BRAND=APPLE,”“TYPE=IPHONE”), identify an order of their presentation, generate a userinterface, and communicate the user interface, at operation “C,” overthe network, back to the client machine. Accordingly, the user interfacemay include the updated concept query (e.g., “COLOR=BLACK,”“BRAND=APPLE,” “TYPE=IPHONE”), a set of values (e.g., “BLUE,” “YELLOW,”“PURPLE,” “GREEN”) for the identified set of filters in the indicatedorder, and search results including listings (not shown) that areidentified based on the concept query, as illustrated in FIG. 1C. Notethat the value “BLACK” is positioned first in the set of values becauseit was selected by the user. The system 30 may identify the filters,including their values and their order of presentation, based on theconcept query and based on probabilities describing occurrences ofattribute-value pairs (e.g., filters) in listings that respectivelydescribe items that were previously transacted on a network-basedmarketplace (e.g., completed listings). That is, each value (e.g.,“BLUE,” “YELLOW,” “PURPLE,” “GREEN”) in a filter and its order ofpresentation may be determined based on a probability of the filter, asan attribute value-pair, occurring in the title of the completedlistings and based on probabilities of the filter, as an attribute-valuepair, co-occurring in the completed listings with each of the otherfilters (e.g., “BRAND=APPLE,” “TYPE=IPHONE”) in the concept query.

FIG. 1D illustrates a schematic of a generation of a popularity table,according to an example embodiment. The popularity table may be utilizedto 1) identify one filter set from multiple filter sets that areidentified based on a keyword query, as illustrated in FIG. 1A; 2)identify one or more filters from multiple filters and their order ofpresentation based on an identified filter set (e.g., conceptquery/filter context), as illustrated in FIG. 1B; 3) identify one ormore filters and their associated values from multiple filters and theorder of the presentation, as illustrated in FIG. 1C; and 4) seed asearch, as illustrated in FIG. 1E. The popularity table may be utilizedby the above features because it presents for immediate real-time accessprobabilities of occurrences of attribute-value pairs (e.g., filters) inthe titles of listings previously transacted in the network-basedmarketplace (e.g., completed listings) and probabilities ofco-occurrences of pairs of attribute-value pairs (e.g., filters) in thetitles of listings previously transacted in the network-basedmarketplace (e.g., completed listings). The former probability isreferred to throughout this document as title probability. The latterprobability is referred to throughout this document as jointprobability.

Broadly, a combination of off-line and on-line steps may be utilized togenerate the popularity table and to make it accessible to the abovefeatures. At operation “A,” a popularity module may sample “completedlistings” from an items table. A “completed listing” may be a listingthat describes an item (e.g., good or service) that was sold (e.g.,purchased/won an auction). For example, the popularity module mayexecute on the network-based marketplace to sample listings that werecompleted (e.g., sold) at a time that occurs during a predeterminedperiod including a start-time and an end-time. Next, at operation “B,”the popularity module may count the number of completed listings (e.g.,ten-thousand listings), the number attribute-value pairs (e.g., filters)according to unique attribute-value pairs (e.g., two-hundred listingswith titles including the attribute-value pair COLOR=RED, two-hundredlistings with titles including the attribute-value pair COLOR=BLUE) inthe titles of the listings, and the number of pairs of attribute-valuepairs (e.g., filter-filter) according to one attribute-value pairco-occurring with another attribute-value pair (e.g., ten listingsincluding titles including the pair of attribute-value pairs “COLOR=RED”and “BRAND=APPLE” (e.g., 10/200=5%)) in the titles of the listings.Next, at operation “C,” the popularity module may generate thepopularity table such that each row corresponds to an attribute-valuepair (e.g., filter). That is, a row may include the attribute-value(e.g., filter), a title probability (e.g., percentage of listingsincluding titles including the attribute-value pair for the row) and oneor more co-occurrence of probabilities for pairs of attribute-valuepairs including the attribute-value-pair designated for the row (e.g.,filter) and another attribute-value pair (co-occurrence attribute-valuepair). The size of joint probability information is not prohibitivebecause the probability of most pairs of filters (e.g., filterattribute-value pair and co-occurrence attribute-value pair) is zero.Finally, at operation “D,” the network-based marketplace may utilize thepopularity table to identify a filter set, identify one or more filtersand their order of presentation, and identify one or more filter valuesand their order of presentation, as described above and throughout thisdocument. Further, at operation “D,” the network-based marketplace mayutilize the popularity table to seed a search, as described below.

FIG. 1E illustrates a fourth aspect of the present disclosure in theform of a system 32 to seed a search, according to an embodiment.Preliminary to the seeding of the search, a network-based marketplacemay be configured with constraints that operate to identify searchresults. For example, the user interface 33 may be generated based onpreexisting constraints including a query (e.g., “IPAD AIR”) and afilter context (e.g., “TYPE=IPAD,” “BRAND=APPLE” (not shown)). Further,the user interface 33 may receive additional preexisting constraints inthe form of selections from a user that identify one or more filters(none shown as selected) in the filter proposal. Accordingly, thenetwork-based marketplace may, in some instances, be configured withpreexisting constraints before the search is seeded with a seed listing.

At operation “A,” a user selects a particular listing in the searchresults from the user interface 33 as a seed listing. The seed listingdescribes an item that is utilized to further refine the search results.It will be appreciated that other examples may utilize other mechanismsto identify a seed listing. Further, it will be appreciated that othermechanisms may not be associated with preexisting constraints.

At operation “B,” the network-based marketplace receives the selectionas a communication over the network, and at operation “C,” thenetwork-based marketplace utilizes the selection to identify the seedlisting in the items table.

At operation “D,” the network-based marketplace identifies a seed filtercontext based on the category (e.g., CATEGORY=COMPUTER) in the seedlisting and preexisting constraints. A category is a filter with theattribute “CATEGORY.” In some examples, the network-based marketplaceutilizes multiple categories to identify the seed filter context. Thepresent example illustrates preexisting constraints. Accordingly, thenetwork-based marketplace may utilize the category (e.g.,CATEGORY=COMPUTER) from the seed listing, the filter context (e.g.,“TYPE=IPAD,” “BRAND=APPLE” (not shown)) from user interface 33, and anyselected filters from the filter proposal to identify the seed filtercontext.

The network-based marketplace initially identifies the seed filtercontext as one or more filters that are not initialized (e.g.,attributes without values) (e.g., “BRAND=,” “CARRIER=,” “MEMORY=” (notshown)). The system 32 identifies the uninitialized filters in the seedfilter context and their order of presentation based on the category(s)in the seed listing, preexisting constraints, and probabilities thatdescribe occurrences of attribute-value pairs (e.g., filters) inlistings that describe items previously transacted on the network-basedmarketplace (e.g., completed listings). That is, each uninitializedfilter in the seed filter context and its order of presentation isdetermined based on a probability of the filter, as an attributevalue-pair, occurring in the completed listings and on probabilities ofthe filter, as an attribute-value pair, co-occurring in the completedlistings with each of the category filter and the filters in thepreexisting constraints.

At operation “E,” the network-based marketplace extracts values from theseed listing based on the attributes of the uninitialized filters in theseed filter context. For example, the network-based marketplace mayutilize the attributes of the uninitialized filters in the seed filtercontext “BRAND=,” “CARRIER=,” “MEMORY=” to identify matching attributesof filters in the structured information in the seed listing (e.g.,“BRAND=APPLE”) and extract the corresponding values (e.g., “APPLE,”“ATT,” “64 GB”).

At operation “F,” the network-based marketplace initializes each of thefilters “BRAND=,” “CARRIER=,” “MEMORY=” in the seed filter context withthe corresponding identified values (e.g., “APPLE,” “ATT,” “64 GB”) thatwere extracted from the seed listing to generate a seed filter context.For example, the seed filter context is initialized “BRAND=APPLE,”“CARRIER=ATT,” and “MEMORY=64 GB.”

At operation “G,” the network-based marketplace identifies a seed filterproposal in the form of a second set of filters for presentation in theuser interface 35. The system 32 may identify the filters in the seedfilter proposal and their order of presentation based on the seed filtercontext and probabilities that describe occurrences of attribute-valuepairs (e.g., filters) in listings that describe items previouslytransacted on the network-based marketplace (e.g., completed listings).That is, each filter in the seed filter proposal and its order ofpresentation may be determined based on a probability of the filter, asan attribute value-pair, occurring in the completed listings and onprobabilities of the filter, as an attribute-value pair, co-occurring inthe completed listings with each of the other filters in the seed filtercontext.

At operation H, the system 32 generates search results by searching theitems table based on the seed filter context (e.g., “BRAND=APPLE,”“CARRIER=ATT,” “MEMORY=64 GB”), the query (e.g., “IPAD AIR”), the filtercontext (e.g., “TYPE=IPAD,” “BRAND=APPLE” (not shown)), and any filtersin the filter proposal that may have been selected by the user. Forexample, the system may generate search results by matching the words inthe query with unstructured text in a listing (e.g., description, title,etc.) and the filters (e.g., the seed filter context, filter context,and selected filters in the filter proposal) with structured informationin the listing, as described further below.

At operation “I,” the system 32 communicates interface information tothe client machine, the interface information being utilized at theclient machine to generate and present the user interface 35. Forexample, the interface information may include a query, seed filtercontext, seed filter proposal, and search results (e.g., sorted), andthe client machine may utilize the interface information to update orgenerate a user interface or portions thereof. In another embodiment theuser interface 35 may be generated at the network-based marketplace.Subsequent to the presentation of user interface 35, the user may selectfilters from the seed filter proposal on user interface 35 to add one ormore additional filters that are utilized to further refine the searchresults. Accordingly, the system 32 generates search results bysearching an items table based on seed item constraints (e.g., seedfilter context, filters selected from seed filter proposal) andpreexisting constraints (e.g., query, filter context, filters selectedfrom filter proposal). The seed item constraints may be cancelled byutilizing a cancelation mechanism provided in an upper right section ofthe seed listing. Some embodiments may receive multiple selectionsrespectively identifying multiple seed listings to seed a search.Accordingly, each seed listing is associated with a set of seed itemconstraints that respectively include one or more filters that areutilized to identify search results.

In some embodiments, the system 32 may sort search results with an itemsimilarity ranking. Item similarity ranking orders the listings in thesearch results from the most similar to the seed listing(s) to the leastsimilar to the seed listing(s). In some embodiments, the user mayprovide one or more criteria for sorting. In such instances, the system32 may select a portion of the search result that is identified as mostsimilar to the seed listing and sort the selected portion of the searchresults according to the criteria (e.g., most recently listed) selectedby the user.

FIG. 2A illustrates a user interface 40, according to an embodiment,with navigation panel 44. The user interface 40 may include a querypanel 42, a navigation panel 44, and a search result panel 46. The querypanel 42 may include an input box 48 that is utilized to receive aquery. The query may include keywords or other keyboard characters. Thequery may be received responsive to receiving a selection of a searchuser interface element 50. Receipt of the query may override theprevious query. The navigation panel 44 may be utilized to displayfilters (not shown) (described later) that are identified based on thequery. The search result panel 46 may be utilized to display searchresults (not shown) including descriptions of items that are publishedon a network-based marketplace.

FIGS. 2B-2D illustrates a user interface 60, according to an embodiment,to process a query interactively. The three figures illustrate asequential processing of a query. FIG. 2B illustrates the user interface60, according to an embodiment, to process a query interactively. Theuser interface 60 may include a query panel 42, a concept query panel62, and a search result panel 46. The query panel 42 may be utilized toreceive a query that was entered into an input box 48. The query mayinclude keywords and/or other keyboard characters. The user interfaceelement 63 may be used to add the query. For example, the input box 48may include a query or a query segment that is received responsive toreceiving a selection of a user interface element 63. The query (e.g.,“RED”) may be parsed to identify one or more filters that are displayedin the concept query panel 62. The search result panel 46 may beutilized to display search results (e.g., descriptions of listings) thatare identified based on a concept query (not shown) in the concept querypanel 62.

FIG. 2C illustrates the user interface 60, according to an embodiment,to process a query interactively. The user interface 60 illustratesprocessing of the query “RED” to identify a filter 66 (e.g., COLOR=RED)now displayed as a concept query 64. Each filter 66 in the concept query64 may be associated with a filter removal checkbox 68. The filterremoval checkbox 68 may be selected to selectively remove a filter 66from the concept query 64. The input box 48 is illustrated to show aquery segment “APPLE IPHONE.” Responsive to receipt of a selection ofthe user interface element 63, the query segment may be parsed toidentify one or more filters 66 that are added to the concept query 64.The search result panel 46 may be utilized to display search resultsthat are identified based on the concept query (e.g., COLOR=RED).

FIG. 2D illustrates the user interface 60, according to an embodiment,to process a query interactively. The user interface 60 is illustratedto demonstrate a previous entry of the query segments “RED” and “APPLEIPHONE” into the input box 48, a parsing of the query “RED” to identifya first filter 66 (e.g., COLOR=RED) and a parsing of the query segment“APPLE IPHONE” to identify a second filter 66 (e.g., BRAND=APPLE) and athird filter 66 (e.g., TYPE=IPHONE) to generate the concept query 64(e.g., COLOR=RED, BRAND=APPLE, TYPE=IPHONE). The search result panel 46may be utilized to display search results (not shown) that areidentified based on the concept query 64 (e.g., COLOR=RED, BRAND=APPLE,TYPE=IPHONE).

FIG. 3A illustrates the user interface 40, according to an embodiment,with a navigation panel 44. The user may enter the query “RED APPLE”into the input box 48 and select the user interface element 50.Responsive to the selection of the user interface element 50, thenetwork-based marketplace may receive the selection and the query “REDAPPLE” and parse the query to identify a filter set in the form of afilter context including a first filter in the form of the filter 66,(e.g., COLOR=RED), and a second filter in the form of the filter 66(e.g., BRAND=APPLE). Next, the network-based marketplace may identify anorder of presentation for the filters 66 in the filter context, identifya second set of filters in the form of a filter proposal, and identifyan order of presentation for the filters in the filter proposal.Finally, the network-based marketplace may generate a user interface 40,and communicate the user interface 40 to a client machine, asillustrated in FIG. 3B. The filter context and the filter proposal maybe displayed in the navigation panel 44, as illustrated in FIG. 3B.

FIG. 3B illustrates the user interface 40, according to an embodiment,with the navigation panel 44. The user interface 40, as illustrated, wasgenerated in accordance with the operations described in FIG. 3A. Theuser interface 40 may include the navigation panel 44 and the searchresult panel 46. The navigation panel 44 may include a filter context 70including two filters 66 (e.g., COLOR=RED, BRAND=APPLE) and a filterproposal 72 including three attributes (e.g., TYPE, CONDITION, ITEMLOCATION) each in association with a set of values that may respectivelybe combined with the corresponding attribute to form an attribute-valuepair (e.g., filter 66). The filter context 70 may be identified and theorder of the filters 66 for the filter context 70 may be identifiedbased on the query and the popularity table (not shown), as describedlater in this document. The filter proposal 72 includes one or morefilters 66 that may be identified based on the filter context 70 and apopularity table (not shown), as described later in this document.Further, an order of presentation of the filters 66 in the filterproposal 72 may be identified based on the filter context 70 and thepopularity table (not shown), as described later in this document. Thesearch result panel 46 may include descriptions of listings that wereidentified based on the filter context 70, as previously described. Eachlisting description may describe an item that is being offered for salevia purchase or auction and may include an image, a title, adescription, number of bids, and the highest bid for auction. Otherembodiments of the user interface 40 may include other information.

FIG. 3C illustrates the user interface 40, according to an embodiment,with the navigation panel 44. The user interface 40, as illustrated, wasgenerated in accordance with the operations described in FIG. 3A andFIG. 3B. The user interface 40 illustrates the selection of the value“CELL PHONE” in association with the attribute “TYPE” to generate thefilter 66 “TYPE=CELL PHONE” for addition to the filter context 70.Responsive to the updating of the filter context 70, the network-basedmarketplace may further update the navigation panel 44 and the searchresult panel 46. For example, the navigation panel 44 may be updatedbased on the new filter context 70 to change the order of thepresentation of the filters 66 in the filter context 70, select adifferent set of attributes for the filter proposal 72, and select adifferent order of presentation of attributes for the filter proposal72. In addition, the values that are associated with the attributes toform filters 66 may be selected and ordered in a manner that is similarto the above described selection of filters 66. For example, the valuesassociated with the filter 66 “TYPE=CELL PHONE” in the filter context 70and their order of presentation may be identified based on the filtercontext 70 (e.g., “TYPE=CELL PHONE,” “COLOR=RED” and “BRAND=APPLE”) andthe popularity table, as described later in this document. Further forexample, the values associated with the filter 66 “COLOR=RED” in thefilter context 70 and their order of presentation may be identifiedbased on the filter context 70 (e.g., “TYPE=CELL PHONE,” “COLOR=RED” and“BRAND=APPLE”) and the popularity table, as described later in thisdocument. Further for example, the values associated with the filter 66“BRAND=APPLE” in the filter context 70 and their order of presentationmay be identified based on the filter context 70 (e.g., “TYPE=CELLPHONE,” “COLOR=RED” and “BRAND=APPLE”) and the popularity table, asdescribed later in this document. Further for example, the values (e.g.,“STANDARD,” “EXPEDITE”) associated with the attribute “SHIPPING” in thefilter proposal 72 and their order of presentation may be identifiedbased on the filter context 70 (e.g., “TYPE=CELL PHONE,” “COLOR=RED” and“BRAND=APPLE”) and the popularity table, as described later in thisdocument. Finally for example, the values (e.g., “USED,” “NEW,” “NOTSPECIFIED”) associated with the attribute “CONDITION” in the filterproposal 72 and their order of presentation may be identified based onthe filter context 70 (e.g., “TYPE=CELL PHONE,” “COLOR=RED” and“BRAND=APPLE”) and the popularity table, as described later in thisdocument.

FIG. 4A illustrates a user interface 60, according to an embodiment, toprocess a query interactively. The user interface 60 may include theconcept query 64 that was identified based on a query. The concept query64 is illustrated as including three filters 66 including “COLOR=RED,”“BRAND=APPLE” and “TYPE=IPHONE.” The user interface 60 may include avalue panel 76. Other than the value “RED,” the value panel 76 mayinclude values that are identified based on the concept query 64.Specifically, values other than “RED,” may be identified based on thefilter BRAND=APPLE, the filter “TYPE=IPHONE,” the attribute “COLOR,” andthe popularity table. Further, the values, other than “RED,” (e.g.,“BLACK,” “BLUE,” “YELLOW,” and “PURPLE”) are presented from left toright in an order that is identified based on the filter 66“BRAND=APPLE,” the filter 66 “TYPE=IPHONE,” the attribute “COLOR,” andthe popularity table. In addition, each of the values is associated withan image 78 that is identified based on the respective values (e.g.,“RED,” “BLACK,” “BLUE,” “YELLOW,” and “PURPLE”), the selected attribute(e.g., “COLOR”), and the remaining filters 66 in the concept query 64(e.g., “BRAND=APPLE” and “TYPE=IPHONE”), as described later in thisdocument. The user interface 60 may include search results.

FIG. 4B illustrates the user interface 60, according to an embodiment,to process a query interactively. The user interface 60 may be updatedwith respect to the concept query 64, the value panel 76, and the searchresults panel 46. For example, the user interface 60 in FIG. 4B may beupdated responsive to the network-based marketplace receiving aselection of the value “BLACK” in the value panel 76 as illustrated inFIG. 4A. Returning to FIG. 4B, the concept query 64 may be updated toreplace the filter “COLOR=RED” with the filter 66 “COLOR=BLACK.” Thevalue panel 76 may be updated to replace the value “RED” with the value“BLACK.” Further, the value panel 76 may be updated to include valuesthat are identified based on the filter “BRAND=APPLE,” the filter“TYPE=IPHONE,” the attribute “COLOR,” and the popularity table. Further,the values, other than “BLACK,” (e.g., “BLUE,” “YELLOW,” “PURPLE,” and“GREEN”) are presented from left to right in an order that is identifiedbased on the filter 66 “BRAND=APPLE,” the filter 66 “TYPE=IPHONE,” theattribute “COLOR,” and the popularity table. In addition, each of thevalues is associated with an image 78 that is identified based on therespective value (e.g., “BLACK,” “BLUE,” “YELLOW,” “PURPLE,” and“GREEN”), the selected attribute (e.g., “COLOR”), the remaining filters66 in the concept query 64 (e.g., “BRAND=APPLE” and “TYPE=IPHONE”) andthe popularity table, as described later in this document. The searchresults panel 46 may be updated to include search results that areidentified based on the concept query 64.

FIG. 4C illustrates a user interface 35, according to an embodiment. Theuser interface 35 is generated in accordance with the operationsdescribed in FIG. 1E. The user interface 35 may be generated responsiveto the selection of a seed listing 90, as previously described. The userinterface 35 includes an input panel 82, a seed panel 84, a categorypanel 86, a navigation panel 44, and a search result panel 46. The inputpanel 82 includes an input box 48 and a search button. The input box 48is used to receive a query comprised of keywords. The search button isselectable to communicate the query to the network-based marketplace.

The seed panel 84 includes a seed filter context 88 and a seed listing90. The seed filter context 88 may be generated based on one or morecategories extracted from the seed listing 90 and preexistingconstraints (optional). The seed filter context 88 includes multiplefilters 66 (e.g., “BRAND=APPLE,” “CARRIER=ATT,” “MEMORY=64 GB”). Theseed filter context 88 is utilized to identify the listings in searchresults in the search result panel 46.

Each filter 66 in the seed filter context 88 may be individuallycancelled with a cancellation mechanism to broaden the scope of thesearch results. For example, responsive to the network-based marketplacereceiving a selection for cancellation of a particular filter 66 in theseed filter context 88, the network-based marketplace removes the filter66 from the seed filter context 88 in the seed panel 84, removes thefilter 66 from the seed filter context 88 in the navigation panel 44,and updates the search results in the search result panel 46.

Further, the seed panel 84 includes a “CLEAR’ button that is selectableto cancel the entire seed filter context 88. For example, responsive tothe network-based marketplace receiving a “CLEAR’ button selection, thenetwork-based marketplace removes the seed filter context 88 from theseed panel 84, updates the search results in the search result panel 46,and removes the seed filter context 88 from the navigation panel 44.Further, in instances where preexisting constraints are present and theseed filter context 88 is cancelled, the network-based marketplaceidentifies the search results based on the preexisting constraints.

The seed listing 90 is a listing in the items table and is presented inthe seed panel 84 as including an image, a title, and any other elementor portions of a listing. The seed listing 90 may be canceled with acancellation mechanism. Responsive to the network-based marketplace areceiving a selection for cancellation of the seed listing 90, thenetwork-based marketplace removes the seed filter context 88 and updateslistings in the search results panel 46 based on the preexistingconstraints.

The navigation panel 44 includes a seed filter context 88 and a seedfilter proposal 92. The seed filter context 88 is described above inassociation with the seed panel 84. Responsive to a cancellation of afilter 66 from the seed filter context 88, the network-based marketplacefurther updates the navigation panel 44 and the search result panel 46,as described above. For example, the navigation panel 44 may be updatedbased on the new seed filter context 88 (e.g., cancel “MEMORY=64 GB”) tochange the presentation of the filters 66 in the seed filter context 88,select a different set and order of attributes for the seed filterproposal 92, and select a different set and order of values for each ofthe attributes in the seed filter proposal 92, as described later inthis document.

The search result panel 46 includes search results and a sort selectionmechanism. The search results may include listings describing items forsale on the network-based marketplace including an image and descriptionincluding a title. The listings are identified based on preexistingconstraints and seed item constraints (e.g., seed filter context,filters selected from seed filter proposal). The preexisting constraintsare optional. That is, a search may be seeded with a seed listingwithout preexisting constraints. The preexisting constraints may includea query, a filter context, and filters that were selected from a filterproposal. The seed item constraints may include a seed filter contextand one or more filters selected from seed filter proposal and the like.In some instances, multiple seed listings are selected that correspondto multiple seed contexts that are utilized to identify search results.The query is matched with unstructured text in a listing and the filtersare matched with structured information in the listing.

The sort selection mechanism is utilized to receive criteria forsorting. For example, the network-based marketplace may receive acriterion to sort the listings in a search result according to price,date of publication, auction closing time, and so forth. Responsive toreceipt of the criterion, the network-based marketplace may combinemultiple sorting schemes. For example, responsive to receipt of acriterion, the network-based marketplace may sort based on 1) itemsimilarity ranking for each listing with the one or more seed listingswith 2) a sorting scheme that is as specified in accordance with thecriterion.

FIG. 4D illustrates a user interface 94, according to an embodiment. Theuser interface 94 is generated responsive to the seeding of a search byan identification of a seed listing 90. The user interface 94 includes aseed listing panel 96, a seed filter panel 98, and a search result panel46. The user interface 94 is generated in substantial accordance withthe processing described in FIG. 1E.

The user interface 94 is different from the user interface 35 as shownin FIG. 4C. One difference is the omission of a navigation panel 44 (notshown). Another difference is the display of search results with acarousel mechanism. For example, the search result panel 46 may utilizea carousel mechanism to display search results that are hidden.According to one embodiment, a swiping motion (e.g., right to left orleft to right) in contact with a touch sensitive screen over the searchresult panel 46 exposes previously hidden listings from the searchresults in the search result panel 46. Other differences are notedbelow. The seed listing panel 96 includes the seed listing including animage, a title, and any other element or portions of a listing, asdescribed further below. The seed listing panel 96 further includes acancellation mechanism 97 that is selectable to cancel the seed listing.For example, responsive to the network-based marketplace receiving aselection for cancellation of the seed listing, the network-basedmarketplace removes the seed filter context and updates listings in thesearch result panel 46 based on preexisting constraints, as previouslydescribed.

The seed filter panel 98 includes a seed filter context 88 and a query(e.g., “IPAD AIR”), both as previously described. The search resultpanel 46 includes search results (e.g., descriptions of listings) thatare identified based on constraints, as previously described. Thelistings in the search result panel 46 may be scrolled with a swipingmotion from left to right or a swiping motion form right to left.

FIG. 5 illustrates a system 100, according to an embodiment. The system100 may be used to implement any of the methods, features, or structuresdescribed in the present application. The system 100 may include clientmachines 102 that are coupled over a network 104 with a network-basedmarketplace 106. The client machines 102 may include handheld devices,desktop computers or any other electronic device capable of electroniccommunication. The network 104 may include a telephone network, atelephone cellar network, the Internet, a wireless network, or anycombination thereof. The network 104 may support analogue and/or digitalcommunications over local, mid-range, or wide area distances. Thenetwork-based marketplace 106 may include any electronic marketplace(e.g., eBay, Amazon, etc.) for buyers and sellers that enable thetransaction of goods and/or services. The network-based marketplace 106may include front-end servers 108, back-end servers 110, databaseservers 112, and databases 114. The client machines 102 may communicateover the network 104 with the front-end server(s) 108 that, in turn,communicate with the back-end server(s) 110 that, in turn, communicatewith database server(s) 112, which store data to the database(s) 114 andretrieve data from database(s) 114.

The front-end server(s) 108 may include a communication module 116 and alisting module 118. The listing module 118 may receive requests from theclient machine 102 that are operated by users (e.g., sellers) beforecommunicating responses back to the users. The requests may includeinformation including text or images for publication in a listing thatdescribes items that are being offered for sale on the network-basedmarketplace 106. The text may be parsed based on rules to structure theinformation. For example, a classification rule may include conditioninformation and supplemental information. The condition information maybe applied to the title of a listing, or any other part of the listing,to identify matching keywords. Responsive to identification of a match,the listing may be supplemented (e.g., tagged) with the supplementalinformation. For example, if a title of a listing includes a word “RED”that matches the condition information “RED” then the listing module 118may tag (e.g., concatenate) the listing with the supplementalinformation “COLOR=RED.” The communicating module 116 may receiverequests from the client machine 102 that are operated by users (e.g.,buyers) and communicate responses back to the users. The requests mayinclude queries, query segments, selections that identify filters,selections that identify values of filters, and other information. Theresponses to the requests may include user interfaces including statuson the requests, the concept query panels 62, the navigation panels 44,the search result panels 46, and other information.

The back-end servers 110 may include a search engine 121, a popularitymodule 128 and a seed module 130. The search engine 121 may include afilter extraction module 122, a filter name module 124, and a filtervalue module 126. The search engine 121 may process search requests,filter selections, and other search related requests. The search engine121 may invoke the filter extraction module 122, responsive to receivinga query, with a query or query segment. The filter extraction module 122may extract filter sets from the query based on classification rules,score the filter sets based on a popularity table 144, and identify afilter set (e.g., the concept query 64/filter context 70) from thefilters sets based on a highest score, as discussed later in thisdocument. The filter name module 124 may receive one or more filters 66(e.g., the concept query 64/filter context 70) and identify one or morefilters based the popularity table 144. The filter value module 126 mayreceive one or more filters 66 and a filter name (e.g., attribute) toidentify one or more filters 66, respectively including values (e.g.,RED, GREEN, BLUE, etc.), based on the popularity table 144. Thepopularity module 128 may be utilized to generate the popularity table144 (see FIG. 7). The seed module 130 may be utilized to seed a searchwith a seed listing. The seed module 130 may identify a seed listing 90,facilitate the identification of a seed filter context 88, extractvalues 168 from the seed listing 90, initialize the seed filter context88, facilitate the identification of a seed filter proposal 92, generatesearch results, and communicate interface information that is utilizedto generate or update displays on client machines.

The databases 114 may include search metadata 140, an items table 142,and the popularity table 144. The search metadata 140 may storeclassification rules and other information used to generate structuredinformation (e.g., the filters 66, attribute-value pairs) based on alisting, a query, or a query segment. The filters 66 that are extractedfrom a query are attribute-value pairs and, accordingly, may be utilizedto match the attribute-value pairs in a listing to generate searchresults. The items table 142 may be used to store listings that describeitems for sale on the network-based marketplace 106. The popularitytable 144 may be generated from a set of completed listings that areextracted from the items table 142 based on criteria included in apredetermined period of time. The popularity table 144 may include rowsthat correspond to attribute-value pairs/filters 66. Each row (e.g.,attribute-value pair/filter 66) may include probability information thatdescribes the popularity of the attribute-value pair/filter 66 in a setof completed listings.

FIG. 6A illustrates the search metadata 140, according to an embodiment.The search metadata 140 may include multiple entries of domaininformation 150. The search metadata 140 describes a set of domains thatmay be utilized to classify listings, describing items being offered forsale on a network-based marketplace 106. A domain may be nested insideof another domain. For example, the domains “home,” “Electronics,” “CellPhone & Accessories” and “Popular Brands” telescope down to a leafcategory that includes the domains “Popular Brands,” “Electronics,”“Cell phone & Accessories.” That is, the domain “home” includes thedomain “Electronics,” which includes the domain “Cell Phone &Accessories,” which includes the domain “Popular Brands,” which includeslistings that describe items for sale on the network-based marketplace106. It will further be appreciated that a domain may include more thanone domain.

FIG. 6B illustrates the domain information 150, according to anembodiment. The domain information 150 may include a domain 152 (e.g.,“home,” “Electronics,” “Television Sets” “Cell Phone & Accessories,”“Popular Brands” etc.), classification rule information 154, andcondition information 156. The classification rule information 154 mayinclude a set of classification rules that are associated with thedomain 152. The condition information 156 may include one or moreBoolean tests that may evaluate TRUE or FALSE. In the present context,the condition information 156 may be utilized to identify the associateddomain 152. For example, the domain 152 “automobiles” may includecondition information 156 to test for the phrase “FORD RANGER” (e.g., if“FORD RANGER”). That is, if the string “FORD RANGER” is matched, thenassert TRUE for the domain 152 of “automobiles.” Other tests may beappropriate for other domains 152 (e.g., toys, phones).

FIG. 6C illustrates the classification rule information 154, accordingto an embodiment. The classification rule information 154 may includemultiple entries of classification rules 160. The classification rules160 may be utilized to structure text in a query, query segment, orlisting.

FIG. 6D illustrates the classification rule 160, according to anembodiment. The classification rule 160 may include conditioninformation 156, as previously described, and supplemental information162. The supplemental information 162 may be utilized responsive to thecondition information 156 evaluating TRUE. For example, the conditioninformation 156 may include the Boolean test, “If ‘RED.’” The conditioninformation 156 may be applied to an identified target (e.g., title inlisting, description in listing, query, query segment, etc.) Further forexample, the condition information 156 may include the Boolean test, “If‘APPLE IPHONE’.” The supplemental information 162 may be utilized tosupplement a listing with an attribute-value pair, the concept query 64with the filter 66, or the filter context 70 with the filter 66responsive to the condition information 156 evaluating TRUE.

FIG. 6E illustrates an attribute-value pair 164, according to anembodiment. The attribute-value pair 164 (e.g., “COLOR=RED”) may includean attribute 166 (e.g., “COLOR”) and a value 168 (e.g., “RED”). Theattribute-value pair 164 may describe an item that is described in alisting 170 (shown in FIG. 6F). The attribute-value pair 164 mayfunction as the filter 66 to identify the listing(s) 170 in the itemstable 142. The attribute 166 may function as a filter name for thefilter 66. The value 168 may correspond to the attribute 166 andfunctions to materialize the attribute 166. For example, the attribute166 “COLOR” corresponds to and materializes the values 168 “BLUE,”“RED,” “GREEN,” and so forth.

FIG. 6F illustrates the items table 142, according to an embodiment. Theitems table 142 may include the listings 170 that are published on thenetwork-based marketplace 106.

FIG. 6G illustrates the listing 170, according to an embodiment. Thelisting 170 may describe an item that is offered for sale on thenetwork-based marketplace 106. The listing 170 may include a title 172,a description 174, structured information 176, an image 78 of the itemthat may be uploaded by the seller, a price 180 to purchase the itemthat may be configured by the seller, a bid 182 being the currenthighest bid for the item, one or more categories 184 (e.g., domain(s)152) where the listing 170 may be found by a user in a navigablehierarchy of categories, a sale completed flag 186, and sale date 188.The title 172 may be a title of the listing 170 and may appear in boldin search results. The title 172 and the description 174 may be receivedfrom the seller. The structured information 176 may include one or moreattribute-value pairs 164. The structured information 176 may beautomatically generated by the network-based marketplace 106 based onthe title 172 or the description 174 based on the classification rules160. The structured information 176 may further be identified based on aselection received from a user (e.g., seller). The sale completed flagmay be asserted TRUE responsive to the sale (e.g., purchased, won in anauction) of the item described by the listing 170, registering thelisting 170 as completed. The sale date 188 is the date the sale wascompleted.

FIG. 6H illustrates structured information 176, according to anembodiment. The structured information 176 may include title structuredinformation 200 including one or more attribute-value pairs 164 and userconfigured structured information 202 including one or moreattribute-value pairs 164. The one or more attribute-value pairs 164included in the title structured information 200 may be generated by thenetwork-based marketplace 106 responsive to parsing the text in thetitle 172 of the listing 170 using the classification rules 160. The oneor more attribute-value pairs 164 included in the user configuredstructured information 202 may be identified based on a selection thatis received from a user. For example, a seller may identify an itemdescribed by a listing 170 as being a particular brand by selecting thebrand (e.g., APPLE) from a user interface to identify theattribute-value pair 164 (e.g., “BRAND=APPLE”).

FIG. 6I illustrates the popularity table 144, according to anembodiment. The popularity table 144 may describe the attribute-valuepairs 164/filters 66 and their relative popularity. Popularattribute-value pairs are associated with probabilities that are greaterthan probabilities associated with unpopular attribute-value pairs. Thepopularity table 144 may be generated based on a set of listings 170that are identified as completed. The popularity table 144 may furtherbe generated by extracting completed listings from the items table 142based on a sale date 188. For example, popularity table 144 may furtherbe generated by extracting completed listings 170 from the items table142 based on a sale date 188 that is identified between a start-date andan end-date. The popularity table 144 may include multiple rows ofattribute-value popularity information 210.

FIG. 6J illustrates the attribute-value popularity information 210,according to an embodiment. The attribute-value popularity information210 may include the filter 66, title probability information 212, andjoint probability information 214. The attribute-value popularityinformation 210 corresponds to a row in the popularity table 144 that,in turn, corresponds to the attribute-value pair 164 that functions asthe filter 66 (e.g., “COLOR=RED”). The title probability information 212may include a title probability 213 that stores the probability ofidentifying the attribute-value pair 164 in the title 172 of the listing170 that is randomly selected from the set of completed listings thatare extracted from the items table 142 to generate the popularity table144. For example, the attribute-value popularity information 210 for thefilter 66 “COLOR=RED” may be computed as 1% based on a set of 10,000completed listings 170 including one-hundred listings 170 includingtitles 172 with the attribute-value pair 164 “COLOR=RED.”

The joint probability information 214 may include multiple filter pairinformation 216 entries. The joint probability information 214 may begenerated to include an entry of the filter pair information 216responsive to identifying the occurrence of the present attribute-valuepair 164 (e.g., filter 66) and another attribute-value pair 164 (e.g.,co-occurrence attribute-value pair 217) in the title 172 of the listing170 that is completed and utilized to generate the popularity table 144.The size of the joint probability information 214 is not prohibitivebecause most pairings of the attribute-value pairs 164 are not found inthe title 172 of the listing 170. The filter pair information 216 mayinclude a co-occurrence attribute-value pair 217 and a joint probability218. For example, joint probability information 214 for theattribute-value pair 164 “COLOR=RED” may include four entries offilter-pair information 216 (e.g., (BRAND=APPLE, 5%) (TYPE=IPHONE, 3%)(CONDITION=NEW, 1%) (ITEM LOCATION=US, 1%).

FIG. 6K illustrates an image table 220, according to an embodiment. Theimage table 220 may include multiple entries of image information 222.The image information 222 may include one or more attribute value pairs164 that describe an associated image 78. For example, the attributevalue pairs “COLOR=BLACK,” “BRAND=APPLE,” and “TYPE=IPHONE” may beassociated with an image 78 of a black cellular phone that is called an“iPhone” that is manufactured by Apple, Inc. of Cupertino, Calif. Alsofor example, the attribute value pairs 164 “COLOR=RED,” “BRAND=APPLE,”and “TYPE=IPHONE” may be associated with an image 78 of a red cellularphone that is called an “iPhone” that is manufactured by Apple, Inc. ofCupertino, Calif.

FIG. 7 illustrates a method 300 to extract completed listings andgenerate the popularity table 144, according to an embodiment. Themethod 300 may commence at operation 302 with the popularity module 128identifying and extracting (e.g., copying) the listings 170 from theitems table 142 that are completed. For example, the popularity module128 may identify and extract (e.g., copy) the listings 170 between apredetermined start-date and a predetermined end date from the itemstable 142 to generate a set of completed listings. For example,responsive to identifying the listing 170 as including the asserted salecompleted flag 186 (e.g., TRUE) and as including the sale date 188between a predetermined start-date and predetermined end-date, thepopularity module 128 may extract (e.g., copy) the listing 170 from theitems table 142 to a set of completed listings.

In operations 303 through 312 the popularity module 128 processes theset of completed listings 170 that were extracted in operation 302. Atoperation 303, the popularity module 128 may advance to the firstlisting 170 in the set of completed listings. At operation 306, thepopularity module 128 may identify attribute-value pairs 164 in thetitle 172 of the listing 170 that is completed. For example, thepopularity module 128 may identify attribute-value pairs 164 in thetitle structured information 200 of the listing 170. At operation 308,the popularity module 128 may increment counts that are associated withattribute-value pairs 164 identified in the listing 170 that iscompleted. For example, the popularity module 128 may increment a countassociated with the attribute value pair 164 “COLOR=RED” responsive toidentifying the attribute value pair 164 “COLOR=RED” in the title 172.Likewise, the popularity module 128 may increment other countsresponsive to identification of other attribute-value pairs beingidentified in the title 172. At operation 310, the popularity module 128may increment counts that are associated with pairs of attribute-valuepair 164 elements found in the title 172 of the listing 170 that iscompleted. For example, the popularity module 128 may increment a countassociated with the pair of attribute value pair 164 elements“COLOR=RED” and “TYPE=CELL PHONE” responsive to a first pair of elementsbeing identified in the title 172 (e.g., “RED APPLE IPHONE FOR SALE”).In a further example, the popularity module 128 may increment the countassociated with the pair of attribute value pair 164 elements“COLOR=RED” and “BRAND=APPLE” responsive to a second pair of elementsbeing identified in the same title 172 (e.g., “RED APPLE IPHONE FORSALE”). Likewise, the popularity module 128 may increment other countsresponsive to identification of other pairs of attribute-value pair 164elements being identified in the title 172 of the listing 170 that iscompleted. According to one embodiment, the counts described inoperations 308 and 310 may be implemented as a table with an X axis anda Y axis that are extended responsive to discovering a new type ofattribute-value pair 164, the cells of the table being utilized to storethe counts associated with identification of an attribute-value pair 164in the title 172 and identification of the pair of attribute-value pair164 elements in the title 172. For example, after processing one-hundredlistings 170, a set of counts may appear as follows:

“COLOR = “TYPE = RED” “BRAND = APPLE” IPHONE” “COLOR = RED” 10 1 2“BRAND = APPLE” 1 12 3 “TYPE = IPHONE” 2 3 11

The cells associated with an intersection of an attribute-value pair 164with itself (e.g., “COLOR=RED” and “COLOR=RED”) may be used to storecounts representing the number of times the attribute-value pair 164 wasidentified in the title 172. The cells associated with an intersectionof the attribute-value pair 164 with another attribute-value pair 164(e.g., “COLOR=RED” and “BRAND-APPLE”) may be used to store countsrepresenting the number of times the attribute-value pair 164 (e.g.,“COLOR=RED”) was identified with the second attribute-value pair 164(e.g., “BRAND-APPLE”) in the title 172. At decision operation 312, thepopularity module 128 may identify whether more listings 170 are presentin the set of completed listings. If more listings 170 are identifiedthen the popularity module 128 may advance to the next listing 170 andbranch to operation 306. Otherwise, processing continues at operation314. At operation 314, the popularity module 128 may generate titleprobabilities and joint probabilities. For example, assume the abovetable represents the counts after one-hundred completed listings havebeen processed. The popularity module 128 may compute probabilities asfollows:

“COLOR = “BRAND = “TYPE = RED” APPLE” IPHONE” “COLOR = 10/100 = 10% 1/10 = 10% 2/10 = 20% RED” “BRAND = 1/12 = 8% 12/100 = 12%  3/12 = 25%APPLE” “TYPE =  2/11 = 18% 3/11 = 27% 11/100 = 11%  IPHONE”

The cells associated with an intersection of the attribute-value pair164 with itself (e.g., “COLOR=RED” and “COLOR=RED”) may be used to storethe title probability 213 (e.g., representing the number of times theattribute-value pair 164 was identified in the title 172). The cellsassociated with the intersection of the attribute-value pair 164 withanother attribute-value pair 164 (e.g., “COLOR=RED” and “BRAND-APPLE”)may be used to store a joint probability 218, representing the number oftimes the attribute-value pair 164 (e.g., “COLOR=RED”) was identifiedwith the second attribute-value pair 164 (e.g., “BRAND-APPLE”) in thetitle 172. At operation 316, the popularity module 128 may generate thepopularity table 144 based on the above computations.

FIG. 8A illustrates a method 400 to identify a filter set in a querycomposed of keywords, according to an embodiment. Illustrated on theleft are operations performed by the client machine 102, illustrated inthe middle are operations performed by the front-end servers 108 in thenetwork-based marketplace 106, and illustrated on the right areoperations performed by the back-end servers 110 in the network-basedmarketplace 106. The method 400 may commence at operation 402 with theclient machine 102 communicating a request including a query over thenetwork 104 to the network-based marketplace 106. The query may includeone or more keywords. For example, the client machine 102 may presentthe user interface 40, as illustrated in FIG. 2A, or the user interface60, as illustrated in FIG. 2B, to receive a query or a query segmentbefore communicating the request over the network 104 to thenetwork-based marketplace 106.

At operation 404, the communication module 116 may receive the requestand communicate the query to the search engine 121 at the back-endservers 110. At operation 406, the search engine 121 may receive andstore the query. At operation 408, the search engine 121 may invoke thefilter extraction module 122 which extracts filter sets from the query.For example, the filter extraction module 122 may parse the query byutilizing the classification rules 160 to identify and extract one ormore filters 66 from the query. The filter extraction module 122 mayextract multiple filter sets from the same query. For example, thefilter extraction module 122 may utilize the search metadata 140 toparse the query “RED APPLE IPHONE” to identify the following four filtersets:

FILTER SET 1) COLOR = RED, BRAND = APPLE, TYPE = CELL PHONE 2) COLOR =RED, BRAND = APPLE, TYPE = IPHONE 3) COLOR = RED, PRODUCT = APPLE IPHONE4) POPULAR PRODUCT = RED APPLE IPHONE

All of the above filter sets are well formed. Nevertheless, the filterextraction module 122 may identify one filter set from the above filtersets as popular based on the popularity table 144. Operation 408 isfurther described in FIG. 8B and FIG. 8C. At operation 410, the filterextraction module 122 may score each of the filter sets by utilizingprobabilities that characterize the popularity of the filters 66 in thepopularity table 144. For example, the filter sets may be scored asfollows:

FILTER SET SCORE 1) COLOR = RED, BRAND = APPLE, TYPE = CELL PHONE 100 2)COLOR = RED, BRAND = APPLE, TYPE = IPHONE 95 3) COLOR = RED, PRODUCT =APPLE IPHONE 93 4) POPULAR PRODUCT = RED APPLE IPHONE 80

Operation 410 is further described in FIG. 8D. At operation 412, thefilter extraction module 122 may identify the filter set with thehighest score (e.g., concept query 64 or the filter context 70). Atoperation 414, the filter extraction module 122 may generate the searchresults based on the filter set with the highest score. For example, thefilter extraction module 122 may generate the search results byidentifying listings 170 in the items table 142 that match the filters66 in the filter set that was identified. In some cases, the filterextraction module 122 may not map every keyword in a query to the filter66. In such cases, the filter extraction module 122 may identify thelistings 170 in the items table 142 that match both the filters 66 inthe filter set with the highest score and the unmapped keywords in thequery. At operation 416, the filter extraction module 122 maycommunicate the search results and the filter set with the highest score(e.g., concept query 64 or filter context 70) to the front-end servers108. At operation 418, the communication module 116 may generate theappropriate user interface (e.g., user interface 40 or user interface60) and communicate the user interface over the network 104 to theclient machine 102. At operation 420, the client machine 102 may displaythe interface.

FIG. 8B illustrates a method 440 to extract filter sets from a query,according to an embodiment. The method 440 may commence, at operation442, with the filter extraction module 122 advancing to the first domaininformation 150 in the search metadata 140. At decision operation 444,the filter extraction module 122 may compare the condition information156 in the domain information 150 with the query to identify whetherclassification rules 160 for a particular domain (e.g., “Electronic HandHeld Devices”) are to be applied to the query. For example, the filterextraction module 122 may compare the word (e.g., “IPHONE”) in thecondition information 156 in the domain information 150 with the wordsin the query (e.g., “APPLE IPHONE”). If the condition evaluates TRUE(e.g., match), then processing continues at operation 446. Otherwise,processing continues at decision operation 452. At operation 446, thefilter extraction module 122 may identify whether the classificationrules 160 associated with the present domain 152 match the query toidentify a filter set. It will be appreciated that multiple filter setsmay be identified before advancing to the next domain 152. The operation446 is further described in FIG. 8C. At decision operation 448, thefilter extraction module 122 may identify whether a filter set wasidentified. If the filter set was identified then processing continuesat operation 450. Otherwise, processing continues at decision operation452. At operation 450, the filter extraction module 122 registers thefilter set as being identified. At decision operation 452, the filterextraction module 122 may identify whether more domain information 150is present in the search metadata 140. If more domain information 150 ispresent in the search metadata 140 then the filter extraction module 122may advance to the next domain information 150 and processing continuesat decision operation 444. Otherwise, the filter extraction module 122may have extracted all filter sets from the query and processing ends.

FIG. 8C illustrates a method 460 to analyze a query, according to anembodiment. The method 460 may commence at operation 461 with the filterextraction module 122 in the search engine 121 advancing to the firstclassification rule 160 in the domain information 150, as previouslyidentified in method 440, illustrated in FIG. 8B. Returning to FIG. 8C,at operation 462, the filter extraction module 122 may identify whetherthe classification rule 160 was previously registered as used (seeoperation 470). If the classification rule 160 was previously registeredas used then a branch is made to decision operation 472 and processingcontinues. Otherwise, processing continues at decision operation 464. Atdecision operation 464, the filter extraction module 122 may compare thecondition information 156 in the classification rule 160 with the queryto identify a match. If the condition information 156 in theclassification rule 160 (e.g., “IPHONE”) matches one or more keywords inthe query (e.g., “APPLE IPHONE”) then processing continues at operation466. Otherwise processing continues at decision operation 472. Atoperation 466, the filter extraction module 122 may add the filter 66 tothe present filter set. For example, the filter extraction module 122may supplement the filter set being processed with the filter 66“TYPE=IPHONE” in the supplemental information 162 of the classificationrule 160. At operation 468, the filter extraction module 122 may removethe keyword(s) from the query that match the condition information 156in the classification rule 160 that is presently being processed. Atoperation 470, the filter extraction module 122 may register theclassification rule 160 that is presently being processed as used in thedomain 152 that is presently being processed. At decision operation 472,the filter extraction module 122 may identify whether moreclassification rules 160 are present in the domain 152. If moreclassification rules 160 are present then the filter extraction module122 may advance to the next classification rule 160 and processingcontinues at decision operation 462. Otherwise, the filter extractionmodule 122 has finished a single pass of analyzing the query utilizingthe classification rule information 154 for the present domain.

FIG. 8D illustrates a method 480 to score filter sets, according to anembodiment. The method 480 may commence at operation 482 with the filterextraction module 122 advancing to the first filter set. Recall, thefilter extraction module 122 previously extracted a set of filter setsfrom the query by utilizing the classification rules 160. For example,the filter extraction module 122 may have extracted two filter sets fromthe query “RED APPLE IPHONE” as follows:

FILTER SET NO. FILTER SET 1 COLOR = RED, BRAND = APPLE, TYPE = CELLPHONE 2 COLOR = RED, PRODUCT = APPLE IPHONE

At operation 484, the filter extraction module 122 may count the filter66 (e.g., COLOR=RED) in the filter set that is presently being processed(e.g., first filter set). At operation 486, the filter extraction module122 may retrieve the title probability 213 from the probability table144 for the filter 66 that is presently being processed. For example,the filter extraction module 122 may identify the attribute-valuepopularity information 210 entry in the probability table 144 with thefilter 66 (e.g., COLOR=RED) that matches the filter 66 that is beingprocessed (e.g., COLOR=RED) and retrieve the title probability 213 fromthe identified attribute-value popularity information 210 entry. Atoperation 488, the filter extraction module 122 may advance to the firstfilter pair information 216 entry in the identified attribute-valuepopularity information 210. At operation 490, the filter extractionmodule 122 may retrieve the joint probability 218 from the filter pairinformation 216 that is being processed. At decision operation 492, thefilter extraction module 122 may identify whether more filter pairinformation 216 entries are present in the filter pair information 216of the filter 66 that is being processed. If another entry of filterpair information 216 is present then a branch is made to operation 490to process the next filter pair information 216. Otherwise, processingcontinues at decision operation 494. At decision operation 494, thefilter extraction module 122 may identify whether more filters 66 (e.g.,BRAND=APPLE) are present in the filter set that is currently beingprocessed. If more filters 66 are present then the filter extractionmodule 122 advances to the next filter 66 before branching to operation484. Otherwise, processing proceeds to operation 496. At operation 496,the filter extraction module 122 may generate a score for the filter setthat is presently being processed. For example, the filter extractionmodule 122 may generate a score based on the number of filters in thefilter set, the title probabilities 213 of the filter set, and the jointprobabilities 218 of the filter set.

According to one embodiment, the filter extraction module 122 maygenerate a score for the filter sets by utilizing native Bayesianprobabilities. According to one embodiment, the filter extraction module122 may sum the probabilities and the count of filters to generate ascore. For example, the filter extraction module 122 may add the numberof filters, the title probabilities, and the joint probabilities togenerate a score. According to another embodiment, the filter extractionmodule 122 may apply a weight to each type of information before summingthe information. For example, the filter extraction module 122 maymultiply the number of filters by a coefficient; multiply the titleprobabilities by a coefficient; and multiply the joint probabilities bya coefficient; followed by adding the three types of informationtogether. According to one embodiment, the coefficients may beconfigurable by a user. According to one embodiment, the filterextraction module 122 may apply weights to the probabilities and thecount as described above before multiplying the respective results togenerate a score. At decision operation 498, the filter extractionmodule 122 may identify whether more filter sets (e.g., COLOR=RED,PRODUCT=APPLE IPHONE) are to be processed. If more filter sets are to beprocessed then the filter extraction module 122 advances to the nextfilter set at operation 484. Otherwise, the method 480 ends.

FIG. 9A illustrates a method 500 to identify and present filters,according to an embodiment. Illustrated on the left are operationsperformed by the client machine 102, illustrated in the middle areoperations performed by the front-end servers 108 in the network-basedmarketplace 106, and illustrated on the right are operations performedby the back-end servers 110 in the network-based marketplace 106. Themethod 500 may commence at operation 502 with the client machine 102communicating a request including a query to over the network 104 to thenetwork-based marketplace 106. The query may include one or morekeywords. For example, the client machine 102 may present the userinterface 40, as illustrated in FIG. 2A, or the user interface 60, asillustrated in FIG. 2B, before receiving and communicating the queryover the network 104 to the network-based marketplace 106.

The operations 504-512 correspond to operations 404-412 in FIG. 8A andthe extended descriptions in FIGS. 8B-8D. Accordingly, the descriptionof operations 404-412 in FIG. 8A and the extended descriptions in FIGS.8B-8D is the same for operations 504-512 in FIG. 9A. At operation 514,the search engine 121 may utilize the identified set of filters in theform of the filter context 70 (e.g., first plurality of filters) toidentify an order of presentation for the filter context 70, asillustrated and described in the user interface 40 in FIG. 3B, accordingto an embodiment. The search engine 121 may identify the order ofpresentation for the filter context 70 based on the popularity table144. For example, the search engine 121 may identify the filters 66“COLOR=RED” and “BRAND=APPLE” are to be presented, from top to bottom ofthe user interface 40, in the order “COLOR=RED” and “BRAND=APPLE.” Theoperation 514 is described further in method 530 on FIG. 9B.

Returning to FIG. 9A, at operation 516, the search engine 121 mayidentify a second plurality of filters and an order of theirpresentation on the user interface 40 as the filter proposal 72, asillustrated and described in user interface 40 in FIG. 3B. For example,the search engine 121 may invoke the filter name module 124 with thefilter context 70 COLOR=RED, BRAND=APPLE to identify a second pluralityof filters 66 that are most popular and an order of their presentation,the filters including attributes 166 (e.g., filter names). The filtername module 124 may identify the filter names that are most popular andan order of their presentation based the popularity table 144. Theoperation 516 is described further in method 540 in FIG. 9C.

At operation 518, the search engine 121 may generate the search resultsbased on the filter context 70. For example, the search engine 121 maygenerate the search results to include descriptions of the listings 170by identifying the listings 170 in the items table 142 with thestructured information 176 that match the filter context 70. In somecases, the search engine 121 may not map every keyword in a query to thefilter 66. In such cases, the search engine 121 may identify listings170 in the items table 142 that match both the filter set and theremaining keywords.

At operation 520, the search engine 121 may communicate the searchresults and the filter context 70 to the front-end servers 108. Atoperation 522, the communication module 116 may generate an interfaceand communicate the interface over the network 104 to the client machine102. For example, the communication module 116 may generate the userinterface 40 on FIG. 3B, according to an embodiment. According to oneembodiment, the communication module 116 may be configured to identify apredetermined number of filters 66 for display. According to oneembodiment, the communication module 116 may be configured to identify atotal of five filters comprised of both the filter context 70 and thefilter proposals 72, as illustrated in user interface 40 on FIG. 3B. Forexample, the communication module 116 may identify for display the twofilters that were selected by the user and the three filters from thefirst three rows of the sort, as illustrated in user interface 40 onFIG. 3B, to combine with the predetermined number of five. Further, thecommunication module 116 may identify for display the order of thefilters 66 on the user interface from top to bottom and the order oftheir respective values from top to bottom based on the previouslydescribed sorts, according to an embodiment. For example, thecommunication module 116 may identify for display the order of thefilters 66 on the user interface from top to bottom and the order oftheir respective values from top to bottom based on the previouslydescribed sorts as illustrated in user interface 40 on FIG. 3B,according to an embodiment.

At operation 524, the client machine 102 may display the interface(e.g., user interface).

FIG. 9B illustrates a method 530 to identify an order of filters in thefilter context 70, according to an example embodiment. The method 530may commence at operation 532 with the search engine 121 identifyingrows in the popularity table 144 that match the filters 66 in the filtercontext 70. If, for example, the filter context 70 includes the filters66 “COLOR=RED,” “BRAND=APPLE” (e.g., user interface 40 in FIG. 3B) thenthe search engine 121 may identify the two rows (e.g., attribute-valuepopularity information 210) in the popularity table 144 with the samefilters 66.

At operation 534, the search engine 121 may sort the identified two rowsof attribute-value popularity information 210 to maximize the titleprobabilities 213 and the joint probabilities 218. For example, thesearch engine 121 may utilize native Bayesian probabilities to identifythe rows that maximize the probabilities. According to one embodiment,the search engine 121 may sum the title probabilities 213 and the jointprobabilities 218 for each row (e.g., attribute-value popularityinformation 210) to generate a score and sort the rows based on thescores. According to another embodiment, the search engine 121 may applya weight to each type of probability before scoring and sorting. Forexample, the search engine 121 may multiply the title probability in arow by a coefficient, multiply each of the co-occurrence probabilitiesin the row by a coefficient, and add the two products together togenerate a score for a row that is utilized to sort the rows. Accordingto one embodiment, the search engine 121 may multiply the two productstogether to generate a score for a row that is utilized to sort therows. According to one embodiment, the coefficients may be configurableby a user.

At operation 536, the search engine 121 may identify an order ofpresentation of the filters 66 in the filter context 70 based on theorder of the sorted rows. If, for example, the rows corresponding to thefilters 66 “BRAND=APPLE” and “COLOR=RED” were to be sorted into theorder “COLOR=RED” followed by “BRAND=APPLE,” (e.g., from highest tolowest scores) then the filters 66 are presented on a user interfacefrom the top to the bottom in the order “COLOR=RED,” and “BRAND=APPLE”in accordance with the highest score. For example, the order“COLOR=RED,” and “BRAND=APPLE” are illustrated in accordance with theorder of a sort in user interface 40 in FIG. 3B.

Returning to FIG. 9B, at operation 538, the search engine 121 mayadvance to the first row in the above mentioned sort (e.g., correspondsto the first filter 66 “COLOR=RED”). At operation 539, the search engine121 may identify the values and their order of display for the filter 66(e.g., row) that is presently being processed by invoking the filtervalue module 126 with the name of the filter 66 and the filter context70. The operation 539 is described further in FIG. 9E.

At decision operation 541, the search engine 121 may identify whethermore rows (filters 66) are present in the sort. If more rows (filters66) are present in the sort, then the search engine 121 advances to thenext row in the sort and a branch is made to operation 539. Otherwise,the method 530 ends.

FIG. 9C illustrates a method 540 to identify filters in the filterproposal 72 and their order of presentation, according to an embodiment.The method 540 may commence at operation 542 with the search engine 121invoking the filter name module 124 with the filter context 70.Responsive to receiving the filter context 70, the filter name module124 may identify rows in the popularity table 144 (e.g., attribute-valuepopularity information 210) that include non-zero joint probabilities218 for each of the filters 66 in the filter context 70. If for examplea filter context 70 includes the filters 66 “COLOR=RED” and“BRAND=APPLE” then the filter name module 124 may identify rows in thepopularity table 144 with non-zero joint probabilities 218 for thefilters 66 “COLOR=RED” and “BRAND=APPLE.”

At operation 544, the filter name module 124 may sort the identifiedrows of attribute-value popularity information 210 to maximize the titleprobabilities 213 and the joint probabilities 218 in each row, asdescribed in operation 534. At operation 546, the filter name module 124may identify a set of unique filter names (e.g., attributes 166) and anorder of presentation from the sorted rows of attribute-value popularityinformation 210. For example, consider TABLE 1 in FIG. 9D. TABLE 1includes six rows of the attribute-value popularity information 210 thatwere sorted to maximize the title probabilities 213 (not shown) and thejoint probabilities 218 (e.g., row 1 maximizes). The filter name module124 may extract rows with duplicate filter names (e.g., attributes 166)from rows illustrated in TABLE 1 to generate TABLE 2. Accordingly, thefilter name module 124 may identify a set of unique filter names in anorder.

Returning to FIG. 9C, at operation 548, the search engine 121 mayadvance to the first row in the above mentioned sort (e.g., correspondsto the first filter 66 “TYPE=CELL PHONE”). At operation 550, the searchengine 121 may identify the values and their order of display for thefilter 66 (e.g., row) that is presently being processed by invoking thefilter value module 126 with the name of the filter 66 (e.g., attribute166) (e.g., “TYPE”) and the filter context 70. The operation 550 isdescribed further in FIG. 9E.

At decision operation 552, the search engine 121 may identify whethermore rows (filters 66) are present in the above mentioned sort. If morerows (filters 66) are present in the sort then the search engine 121advances to the next row in the sort followed by a branch to operation550. Otherwise, the method 540 ends.

FIG. 9E illustrates a method 560 to identify an ordered set of values168 for a filter name. The method 560 may commence at operation 562 withthe filter value module 126 receiving the filter name (e.g., attribute166) (e.g., “COLOR”) and the set of filters 66. For example, the set offilters 66 may include the filters 66 in the filter context 70 or thefilters 66 in the concept query 64 (e.g., “COLOR=BLACK,” “BRAND=APPLE,”“TYPE=IPHONE”) or filters 66 in a seed filter context 88 (e.g.,“BRAND=APPLE,” “CARRIER=ATT,” “MEMORY=64 GB.” Continuing with operation562, the filter value module 126 may identify a first set of rows (e.g.,attribute-value popularity information 210) in the popularity table 144with the filters 66 that include the attributes 166 that match thefilter name (e.g., “COLOR”) received by the filter value module 126. If,for example, the filter name received by the filter value module 126 is“COLOR,” then all rows that include the filters 66 with the attribute166 “COLOR” are identified as matched. For example, consider TABLE 3 inFIG. 9F, according to an embodiment. FIG. 9F may illustrate at least sixrows of attribute-value popularity information 210 in the popularitytable 144. The filter name module 124 may extract rows from TABLE 3 inFIG. 9F that include attributes the 166 that match the filter name“COLOR” to generate a first set of rows, as illustrated in TABLE 4,according to an embodiment.

Returning to FIG. 9E, at operation 564, the filter value module 126 mayidentify a second set of rows from the first set of rows based on theset of filters 66 received by the filter value module 126. For example,the filter value module 126 may identify a second set of rows from thefirst set of rows where each row includes a non-zero joint probability218 corresponding to the filters 66 in the set of filters 66 received bythe filter value module 126. For example, assume the filter value module126 received the set of filters 66 “COLOR=BLACK,” “BRAND=APPLE,”“TYPE=IPHONE.” The filter value module 126 may identify rows five andsix, as illustrated in TABLE 5 of FIG. 9G, according to an embodiment,because both rows include a non-zero joint probability 218 for thefilter 66 “BRAND=APPLE” and the filter 66 “TYPE=IPHONE.” Note that row 1from TABLE 4 in FIG. 9F was not included in the results because itincluded the joint probability 218 for the attribute-value pair 164“BRAND=APPLE” of zero.

Returning to FIG. 9E, at operation 566, the filter value module 126 maysort the identified rows of attribute-value popularity information 210to maximize the title probabilities 213 and the joint probabilities 218.For example, the filter value module 126 may utilize native Bayesianprobabilities to identify the rows that maximize the probabilities 213,218. According to one embodiment, the filter value module 126 may sumthe title probabilities 213 and the joint probabilities 218 for each row(e.g., attribute-value popularity information 210) to generate a scoreand sort the rows based on the scores. According to another embodiment,the filter value module 126 may apply a weight to each type ofprobability before scoring and sorting. For example, the filter valuemodule 126 may multiply the title probability 213 in a row by acoefficient, multiply each of the joint probabilities 218 in the row bya coefficient, and add the products together to generate a score for arow that is utilized to sort the rows. According to one embodiment, thefilter value module 126 may multiply the products together to generate ascore for a row that is utilized to sort the rows. According to oneembodiment, the coefficients may be configurable by a user. At operation568, the filter value module 126 may identify the order of the filtervalues. For example, the order of the filter values may be identifiedbased on the result of the sort in operation 566 (e.g. first row=highestscore, second row=second highest score).

FIG. 10A illustrates a method 600 to identify values 168 for a selectedfilter, according to an embodiment. Illustrated on the left areoperations performed by the client machine 102, illustrated in themiddle are operations performed by the front-end servers 108 in thenetwork-based marketplace 106, and illustrated on the right areoperations performed by the back-end servers 110 in the network-basedmarketplace 106. Prior to commencing the method 600, the network-basedmarketplace 106 may process a selection of a filter “COLOR=RED” in theconcept query 64 (e.g., “COLOR=RED,” “BRAND=APPLE,” and “TYPE=IPHONE”)to generate the user interface 60, as illustrated in FIG. 4A. The userinterface 60 is shown to illustrate the value panel 76 presenting a setof colors (e.g., values 168) “RED,” “BLACK,” “BLUE” “YELLOW” AND“PURPLE.”

The method 600 may commence at operation 602 with the client machine 102receiving and communicating a request including a selection thatidentifies the filter 66 (e.g., “COLOR=BLACK”) over the network 104 tothe network-based marketplace 106. For example, as illustrated in FIG.4A and FIG. 10A, a user may select “BLACK” from the value panel 76 ofthe user interface 60 identifying the filter 66 “COLOR=BLACK” andcausing the client machine 102 to communicate a request including afirst attribute-value pair 164 “COLOR=BLACK” to the network-basedmarketplace 106.

At operation 604, at the network-based marketplace 106, thecommunication module 116 at the front-end servers 108 may receive andcommunicate the selection to the back-end servers 110. At operation 606,the search engine 121, at the back-end servers 110, may receive theselection. At operation 608, the search engine 121 may identify valuesand their order of display for the filter 66 (COLOR=BLACK) by invokingthe filter value module 126 with the name of the filter 66 (e.g.,attribute 166) (e.g., “COLOR”) and the concept query (e.g.,“COLOR=BLACK,” “BRAND=APPLE,” and “TYPE=IPHONE”). The filter valuemodule 126 may identify values for the filter name “COLOR” and an orderof their presentation based on the concept query 64. For example, thefilter value module 126 may identify, order, and return a plurality offilters as follows: “COLOR=BLUE,” “COLOR=YELLOW,” “COLOR=PURPLE,”“COLOR=GREEN,” etc. that respectively include a plurality of values“BLUE,” “YELLOW,” “PURPLE,” “GREEN,” etc. The operation 608 may furtherbe described in FIG. 9E.

At operation 610, the search engine 121 may generate search results, aspreviously described, based on the concept query 64 “COLOR=BLACK,”“BRAND=APPLE,” and “TYPE=IPHONE.” At operation 612, the search engine121 may communicate the search results and the plurality of filters(e.g., “COLOR=BLUE,” “COLOR=YELLOW,” “COLOR=PURPLE,” “COLOR=GREEN”) tothe front-end servers 108.

At operation 614 the front-end servers 108 may generate a user interface(e.g., user interface 60) based on the first plurality of filter valuesand the concept query 64. For example, the concept query 64 may beupdated to include the filters 66 “COLOR=BLACK,” “BRAND APPLE,” and“TYPE=IPHONE.” As illustrated in FIG. 4B, the images 78 may beidentified for the values in the value panel 76 and a user interfacegenerated. At operation 614, the communication module 116 may generatean interface. For example, the communication module 116 may generate theuser interface 60, as illustrated in FIG. 4B. The operation 614 isfurther described in FIG. 10B. At operation 616, the client machine 102may receive and display the user interface.

FIG. 10B illustrates a method 700 to generate an interface includingfilter values and images, according to an embodiment. The method 700 maycommence at operation 702 with the communication module 116 identifyingthe image 78 for the filter 66 that was selected. For example, thecommunication module 116 may identify the image information 222 in theimage table 220 that includes attribute-values pairs 164 that match thefilter 66 that was selected, “COLOR=BLACK,” and the other filters 66 inthe concept query 64, “BRAND=APPLE,” “TYPE=IPHONE.” Responsive toidentifying the match, the communication module 116 may retrieve theassociated image 78 (e.g., black Apple iPhone).

At operation 706, the communication module 116 may identify the firstfilter 66. For example, the communication module 116 may identify thefirst row (e.g., highest sorted priority) (ROW1−“COLOR=BLUE”) of therows of the attribute-value popularity information 210 that werepreviously identified by the filter value module 126. Recall that thefilter value module 126 identified and sorted a set of rows ofattribute-value popularity information 210 where each row corresponds tothe filter 66, as described in FIG. 9E.

Returning to FIG. 10B, at operation 710, the communication module 116may identify the image 78 for the filter 66 that is being processed andthe other filters 66 in the concept query 64. For example, thecommunication module 116 may identify image information 222 in the imagetable 220 that includes attribute-values pairs 164 that match theconcept query 64 “BRAND=APPLE,” “TYPE=IPHONE” and the present filter(e.g., “COLOR=BLUE”).

At decision operation 712, the communication module 116 may identifywhether more filters 66 (e.g., rows) are to be processed based on apredetermined threshold. For example, the predetermined threshold may befive (e.g., “COLOR=BLACK,” “COLOR=BLUE,” “COLOR=YELLOW,” “COLOR=PURPLE,”COLOR=GREEN”), as illustrated in FIG. 4B. If more filters 66 are to bedisplayed on the user interface then processing continues with the nextfilter 66 (e.g., row) at decision operation 714. Otherwise, processingcontinues at operation 716. At operation 716, the communication module116 may generate an interface based on the search results, the valuesfor the filters 66, the images 78 associated with the filters 66, andthe concept query 64. For example, the communication module 116 maygenerate the user interface 60, as illustrated on FIG. 4B.

FIG. 10C illustrates a method 800 to seed a search, according to anembodiment. Illustrated on the left are operations performed by theclient machine 102, illustrated in the middle are operations performedby the front-end servers 108 in the network-based marketplace 106, andillustrated on the right are operations performed by the back-endservers 110 in the network-based marketplace 106. The method 800commences at operation 802 with the client machine 102 communicating amessage including a selection that identifies a listing 170 as a seedlisting 90 to the network-based marketplace 106. For example, the clientmachine 102 may present the user interface 33, as illustrated in FIG. 1Eto receive the message selection that identifies the listing 170 as aseed listing 90 and communicate the message over the network 104 to thenetwork-based marketplace 106. At operation 804, the communicationmodule 116 receives the message and communicates the message to thesearch engine 121 at the back-end servers 110. At operation 805, thesearch engine 121 receives the message and identifies the listing 170 inthe items table 142 as the seed listing 90.

At operation 806, the seed module 130 identifies the seed filter context88. The seed module 130 may identify the seed filter context 88 based ona category 184 (e.g., CATEGORY=COMPUTER) that is extracted from the seedlisting 90. In addition, the seed module 130 may utilize preexistingconstraints to identify the seed filter context 88. Preexistingconstraints are constraints that are utilized to identify search resultsbefore the selection of the seed listing 90. For example, thepreexisting constraints may include a query (e.g., “IPAD AIR”), a filtercontext (e.g., “TYPE=IPAD,” “BRAND=APPLE”), and filter(s) selected froma filter proposal 72 (e.g., SHIPPING=STANDARD). Accordingly, the seedmodule 130 may identify the seed filter context 88 by invoking thefilter name module 124 with the category (e.g., CATEGORY=COMPUTER) fromthe seed listing 90, the filters 66 from the filter context 70 (e.g.,“TYPE=IPAD,” “BRAND=APPLE”) and one or more filters 66 selected from thefilter proposal 72 (e.g., SHIPPING=STANDARD) to identify a plurality offilters 66 that are most popular and an order of their presentation,with the filters 66 including attributes 166 (e.g., filter names).

The filter name module 124 identifies the filter names 166 that are mostpopular and an order of their presentation based on the popularity table144. The system 32 may identify the filters 66 in the seed filtercontext 88 and their order of presentation based on the one or morecategories from the seed listing 90, filters 66 in the preexistingconstraints, and probabilities that describe occurrences ofattribute-value pairs (e.g., filters) in listings 170 that describeitems previously transacted on the network-based marketplace 110 (e.g.,completed listings 170). That is, each filter 66 in the seed filtercontext 88 and its order of presentation may be determined based on aprobability of the filter 66, as an attribute value-pair 164, occurringin the completed listings 170 and on probabilities of the filter 66, asan attribute-value pair 164, co-occurring in the completed listings 170with each of the one or more categories from the seed listing 90 and thefilters 66 in the preexisting constraints. The seed module 130 mayidentify the plurality of filters 66 and an order of their presentationon the user interface 40 as the seed filter context 88, as illustratedand described in user interface 35 in FIG. 4C. The operation 806 isdescribed further in method 840 in FIG. 10D. In the present example, themethod 840 returns a seed filter context 88 with filters 66 notinitialized with values 168 (e.g., “BRAND=,” “CARRIER=,” “MEMORY=”). Inother examples, the seed listing 90 may include more than one categorythat is passed to the filter name module 124. In one embodiment, theselection of multiple seed listings 90 causes an iterative invocation ofthe filter name module 124 to generate multiple seed filter contexts 88.

At operation 807, the seed module 130 extracts values 168 from the seedlisting 90 based on the attributes 166 of the seed filter context 88.For example, the seed module 130 utilizes the attributes 166 of theuninitialized filters 66 in the seed filter context 88 (e.g., “BRAND=,”“CARRIER=,” “MEMORY=”) to identify filters 66 with matching attributes166 in the structured information 176 (e.g., “BRAND=APPLE”) of the seedlisting 90 to extract the identified values 168 (e.g., “APPLE,” “ATT,”“64 GB”).

At operation 808, the seed module 130 initializes each of the filters“BRAND=,” “CARRIER=,” “MEMORY=” in the seed filter context 88 withvalues (e.g., “APPLE,” “ATT,” “64 GB”) that were extracted from the seedlisting 90. For example, the seed module 130 initializes each of thefilters “BRAND=,” “CARRIER=,” and “MEMORY=” to “BRAND=APPLE,”“CARRIER=ATT,” and “MEMORY=64 GB.”

At operation 810, the seed module 130 identifies a seed filter proposal92 in the form of a set of filters 66 for presentation in the userinterface 35. The system 32 may identify the filters 66 in the seedfilter proposal 92 and their order of presentation based on the seedfilter context 88 and probabilities that describe occurrences ofattribute-value pairs (e.g., filters) in listings 170 that describeitems previously transacted on the network-based marketplace 110 (e.g.,completed listings 170). That is, each filter 66 in the seed filterproposal 92 and its order of presentation may be determined based on aprobability of the filter 66, as an attribute value-pair 164, occurringin the completed listings 170, and on probabilities of the filter 66, asan attribute-value pair 164, co-occurring in the completed listings 170with each of the other filters 66 in the seed filter context 88. Theseed module 130 may identify the plurality of filters 66 and an order oftheir presentation on the user interface 40 as the seed filter proposal92, as illustrated and described in user interface 35 in FIG. 4C. Forexample, the seed module 130 may invoke the filter name module 124 withthe seed filter context 88 “BRAND=APPLE,” “CARRIER=ATT,” and “MEMORY=64GB” to identify a plurality of filters 66 that are most popular and anorder of their presentation, with the filters including attributes 166(e.g., filter names). The filter name module 124 may identify the filternames that are most popular and an order of their presentation based onthe popularity table 144. The operation 516 is described further inmethod 940 in FIG. 10E.

At operation 812, the seed module 130 generates search results bysearching the items table 142 to identify listings 170. For example, theseed module 130 identifies listings 170 that match the preexistingconstraints and seed item constraints. Recall that the preexistingconstraints include a query (e.g., “IPAD AIR”), a filter context 70(e.g., “TYPE=IPAD,” “BRAND=APPLE”), and a filter 66 selected from thefilter proposal (e.g., “SHIPPING=STANDARD). The seed module 130 matchesthe query in the preexisting constraints with unstructured informationin the listing 170 and the filters 66 in the filter context 70 and thefilter 66 in the filter proposal 72 with the structured information 176in the listing 170. The seed item constraints may include the seedfilter context 88 (e.g., “BRAND=APPLE,” “CARRIER=ATT,” “MEMORY=64 GB”)and filter(s) 66 that were selected from the seed filter proposal (e.g.,“CONDITION=NEW). The seed module 130 matches seed item constraints withstructured information 176 in the listing 170. Recall that someembodiments utilize multiple seed listings 90. In such instances, theseed item constraints include a seed filter context 88 for each seedlisting 90 and filters 66 selected from multiple seed filter proposals.

The seed module 130 sorts the search results according to an itemsimilarity ranking Item similarity ranking orders the listings in thesearch results from the most similar to the seed listing(s) to the leastsimilar to the seed listing(s). In some embodiments, the user mayprovide one or more additional criteria for sorting. In such instances,the seed module 130 selects a portion of the search results that isidentified as most similar to the seed listing and sort the selectedportion of the search results according to the selected criterion.

At operation 814, the seed module 130 communicates interface informationof the search results to the front-end servers 108. The interfaceinformation may include any information for generating the userinterface 35 or the user interface 94. At operation 816, thecommunication module 116 receives and communicates the interfaceinformation over the network 104 to the client machine 102. According toone embodiment, the communication module 116 is configured to identify apredetermined number of filters 66 in the seed filter context 88 and theseed filter proposal 92. According to one embodiment, the communicationmodule 116 is configured to identify a total of five filters comprisedof both the seed filter context 88 and the seed filter proposal 92, asillustrated in user interface 350 on FIG. 4C. At operation 818, theclient machine 102 receives the interface information and utilizes theinterface information to display or update an interface (e.g., userinterface). For example, the interface information may be utilized todisplay a user interface at the client machine 102 or update a userinterface at the client machine 102. The interface information may beutilized to display or update the user interface 35, as shown on FIG.4C, or the user interface 94, as shown on FIG. 4D. In another embodimentthe interface information may be utilized by the front end serves 108 togenerate the user interface before communicating the user interface(user interface 35, user interface 94) to the client machine 102.

FIG. 10D illustrates a method 840 to identify filters 66 in a seedfilter context 72 and their order of presentation, according to anembodiment. The method 840 commences at operation 842 with the seedmodule 130 invoking the filter name module 124 with one or morecategories 194 of the seed listing 90 and filters 66 from preexistingconstraints. Recall that preexisting constraints may sometimes not bepresent. In the present example, the category is “CATEGORY=COMPUTER” andthe filters 66 from preexisting constraints include the filters 66 inthe filter context 70 (e.g., “TYPE=IPAD,” “BRAND=APPLE”) and a filter 66that was selected from the filter proposal (e.g., SHIPPING=STANDARD).Responsive to receiving the category 194 and the preexistingconstraints, the filter name module 124 may identify rows in thepopularity table 144 (e.g., attribute-value popularity information 210)that include non-zero joint probabilities 218 for each of filters 66passed to the filter name module 124 (e.g., “CATEGORY=COMPUTER,”“TYPE=IPAD,” “BRAND=APPLE,” and “SHIPPING=STANDARD”). If, for example,the filters 66 passed to the filter name module 124 include“CATEGORY=COMPUTER,” “TYPE=IPAD,” “BRAND=APPLE,” and“SHIPPING=STANDARD,” then the filter name module 124 may identify rowsin the popularity table 144 with non-zero joint probabilities 218 forthe filters 66 “CATEGORY=COMPUTER,” “TYPE=IPAD,” “BRAND=APPLE,” and“SHIPPING=STANDARD.”

At operation 844, the filter name module 124 sorts the identified rowsof attribute-value popularity information 210 to maximize the titleprobabilities 213 and the joint probabilities 218 in each row(maximizing is described in operation 534 in FIG. 9B). At operation 846,the filter name module 124 identifies a set of unique filter names(e.g., attributes 166) and an order of presentation from the sorted rowsof attribute-value popularity information 210. For example, considerTable 6 in FIG. 10F. Table 6 includes six rows of the attribute-valuepopularity information 210 that were sorted to maximize the titleprobabilities 213 (not shown) and the joint probabilities 218 (e.g., row1 maximizes). Each row corresponds to a filter 66. Each filter 66includes an attribute (e.g., filter name) 166, a value 168 inassociation with joint probabilities 218. The filter name module 124 mayextract rows with duplicate filter names (e.g., attributes 166) fromrows illustrated in Table 6 to generate Table 7 in FIG. 10G. Table 7includes the same elements as described with respect to Table 6 (e.g.,filter 66, joint probabilities 218). Accordingly, the filter name module124 may identify a set of unique filter names in an order.

Returning to FIG. 10D, at operation 848, the seed module 130 returns aset of filters 66 including attributes 166 without values 168 in theform of a seed filter context 88. In the present example,notwithstanding the examples utilized in Table 6 and Table 7, the filtername module 124 returns the seed filter context 88 with theuninitialized filters 66 “BRAND=,” “CARRIER=,” and “MEMORY=.”

FIG. 10E illustrates a method 940 to identify filters in a seed filterproposal 92 and their order of presentation, according to an embodiment.The method 940 commences at operation 942 with the seed module 130invoking the filter name module 124 with the seed filter context 88.Responsive to receiving the seed filter context 88, the filter namemodule 124 identifies rows in the popularity table 144 (e.g.,attribute-value popularity information 210) that include non-zero jointprobabilities 218 for each of the filters 66 in the seed filter context88. If, for example, a seed filter context 88 includes the filters 66“BRAND=APPLE,” “CARRIER=ATT,” and “MEMORY=64 GB,” then the filter namemodule 124 may identify rows in the popularity table 144 with non-zerojoint probabilities 218 for the filters 66 “BRAND=APPLE,” “CARRIER=ATT,”and “MEMORY=64 GB.”

At operation 944, the filter name module 124 sorts the identified rowsof attribute-value popularity information 210 to maximize the titleprobabilities 213 and the joint probabilities 218 in each row(maximization is described in operation 534 in FIG. 9B). At operation946, the filter name module 124 identifies a set of unique filter names(e.g., attributes 166) and an order of presentation from the sorted rowsof attribute-value popularity information 210. For example, considerTable 8 in FIG. 10H. Table 8 includes six rows of the attribute-valuepopularity information 210 that were sorted to maximize the titleprobabilities 213 (not shown) and the joint probabilities 218 (e.g., row1 maximizes). Table 8 includes the same elements as described in Table 6(e.g., filter 66, joint probabilities 218). The filter name module 124may extract rows with duplicate filter names (e.g., attributes 166) fromrows illustrated in Table 8 to generate Table 9 on FIG. 10I. Table 9includes the same elements as described in Table 6 (e.g., filter 66,joint probabilities 218). Accordingly, the filter name module 124 mayidentify a set of unique filter names in an order.

Returning to FIG. 10E, at operation 946, the seed module 121 advances tothe first row in the above mentioned sort (e.g., corresponds to thefirst filter 66 (e.g., “TYPE=CELL PHONE”)). At operation 950, the seedmodule 130 identifies the values 168 and their order of display for thefilter 66 (e.g., row) that is presently being processed by invoking thefilter value module 126 with the name of the filter 66 (e.g., attribute166) (e.g., “TYPE”) and the seed filter context 88. The operation 950 isdescribed further in FIG. 9E.

Returning to FIG. 10E, at decision operation 952, the seed module 121identifies whether more rows (filters 66) are present in the abovementioned sort. If more rows (filters 66) are present in the sort, thenthe seed module 121 advances to the next row in the sort followed by abranch to operation 950. Otherwise, the method 940 ends.

FIG. 11 illustrates network architecture 1100, according to anembodiment. A networked system 1102, in an example form of anetwork-server-side functionality, is coupled via a communicationnetwork 1104 (e.g., the Internet, wireless network, cellular network, ora Wide Area Network (WAN)) to one or more client devices 1110 and 1112.The networked system 1102 corresponds to the system 100 in FIG. 5, thecommunication network 1104 corresponds to the network 104 in FIG. 5, andthe client devices 1110 and 1112 correspond to the client machines 102in FIG. 5, accordingly, the same or similar references have been used toindicate the same or similar features unless otherwise indicated. FIG.11 illustrates, for example, a web client 1106 operating via a browser(e.g., such as the INTERNET EXPLORER® browser developed by Microsoft®Corporation of Redmond, Wash. State), and a programmatic client 1108executing on respective client devices 1110 and 1112.

The network architecture 1100 may be utilized to execute any of themethods described in this document. The client devices 1110 and 1112 maycomprise a mobile phone, desktop computer, laptop, or any othercommunication device that a user may utilize to access the networkedsystem 1102. In some embodiments, the client device 1110 may comprise adisplay module (not shown) to display information (e.g., in the form ofuser interfaces). In further embodiments, the client device 1110 maycomprise one or more of a touch screen, accelerometer, camera,microphone, and GPS device. The client devices 1110 and 1112 may be adevice of a user that is used to perform a transaction involving digitalgoods within the networked system 1102. In one embodiment, the networkedsystem 1102 is a network-based marketplace that manages digital goods,publishes publications comprising item listings of products available onthe network-based marketplace, and manages payments for thesemarketplace transactions. Additionally, external sites 1128, 1128′ maybe sites coupled to networked system 1102 via network 1104. Externalsites may be any desired system, including ecommerce systems.

An Application Program Interface (API) server 1114 and a web server 1116are coupled to, and provide programmatic and web interfaces respectivelyto, one or more application servers 1118. The application server(s) 1118host a publication system 1200 and a payment system 1122, each of whichmay comprise one or more modules, applications, or engines, and each ofwhich may be embodied as hardware, software, firmware, or anycombination thereof. The application servers 1118 are, in turn, coupledto one or more database servers 1124 facilitating access to one or moreinformation storage repositories or database(s) 1126. In one embodiment,the databases 1126 are storage devices that store information to beposted (e.g., publications or listings) to the publication system 1200.The databases 1126 may also store digital goods information inaccordance with example embodiments.

In example embodiments, the publication system 1200 publishes content ona network (e.g., Internet). As such, the publication system 1200provides a number of publication and marketplace functions and servicesto users that access the networked system 1102. The publication system1200 is discussed in more detail in connection with FIG. 12. In exampleembodiments, the publication system 1200 is discussed in terms of anonline marketplace environment. However, it is noted that thepublication system 1200 may be associated with a non-marketplaceenvironment such as an informational (e.g., search engine) or socialnetworking environment.

The payment system 1122 provides a number of payment services andfunctions to users. The payment system 1122 allows users to accumulatevalue (e.g., in a commercial currency, such as the U.S. dollar, or aproprietary currency, such as points, miles, or other forms of currencyprovide by a private entity) in their accounts, and then later to redeemthe accumulated value for products (e.g., goods or services) that aremade available via the publication system 1200 or elsewhere on thenetwork 1104. The payment system 1122 also facilitates payments from apayment mechanism (e.g., a bank account, PayPal™, or credit card) forpurchases of items via any type and form of a network-based marketplace.

While the publication system 1200 and the payment system 1122 are shownin FIG. 11 to both form part of the networked system 1102, it will beappreciated that, in alternative embodiments, the payment system 1122may form part of a payment service that is separate and distinct fromthe networked system 1102. Additionally, while the example networkarchitecture 1100 of FIG. 11 employs a client-server architecture, askilled artisan will recognize that the present disclosure is notlimited to such an architecture. The example network architecture 1100can equally well find application in, for example, a distributed orpeer-to-peer architecture system. The publication system 1200 andpayment system 1122 may also be implemented as standalone systems orstandalone software programs operating under separate hardwareplatforms, which do not necessarily have networking capabilities.

Referring now to FIG. 12, an example block diagram illustrating multiplecomponents that, in one embodiment, are provided within the publicationsystem 1200 of the networked system 1102 is shown. In this embodiment,the publication system 1200 is a marketplace system where items (e.g.,goods or services) may be offered for sale and that further implementsthe features described herein for interactive query generation andrefinement. The items may comprise digital goods (e.g., currency,license rights). The publication system 1200 may be hosted on dedicatedor shared server machines (not shown) that are communicatively coupledto enable communications between the server machines. The multiplecomponents themselves are communicatively coupled (e.g., via appropriateinterfaces), either directly or indirectly, to each other and to variousdata sources, to allow information to be passed between the componentsor to allow the components to share and access common data. Furthermore,the components may access the one or more databases 1126 via the one ormore database servers 1124, as shown in FIG. 11.

Returning to FIG. 12, the publication system 1200 provides a number ofpublishing, listing, and price-setting mechanisms whereby a buyer maylist (or publish information concerning) goods or services for sale, abuyer can express interest in or indicate a desire to purchase suchgoods or services, and a price can be set for a transaction pertainingto the goods or services. To this end, the publication system 1200 maycomprise at least one publication engine 1202 and one or more auctionengines 1204 that support auction-format listing and price settingmechanisms (e.g., English, Dutch, Chinese, Double, Reverse auctions,etc.).

A pricing engine 1206 supports various price listing formats. One suchformat is a fixed-price listing format (e.g., the traditional classifiedadvertisement-type listing or a catalog listing). Another formatcomprises a buyout-type listing. Buyout-type listings (e.g., theBuy-It-Now (BIN) technology developed by eBay Inc., of San Jose, Calif.)may be offered in conjunction with auction-format listings and allow abuyer to purchase goods or services, which are also being offered forsale via an auction, for a fixed price that is typically higher than astarting price of an auction for an item.

A store engine 1208 allows a buyer to group listings within a “virtual”store, which may be branded and otherwise personalized by and for thebuyer. Such a virtual store may also offer promotions, incentives, andfeatures that are specific and personalized to the buyer. In oneexample, the buyer may offer a plurality of items as Buy-It-Now items inthe virtual store, offer a plurality of items for auction, or acombination of both.

A reputation engine 1210 allows users that transact, utilizing thenetworked system 1102, to establish, build, and maintain reputations.These reputations may be made available and published to potentialtrading partners. Because the publication system 1200 supportsperson-to-person trading between unknown entities, in accordance withone embodiment, users may otherwise have no history or other referenceinformation whereby the trustworthiness and credibility of potentialtrading partners may be assessed. The reputation engine 1210 allows auser, for example through feedback provided by one or more othertransaction partners, to establish a reputation within the network-basedmarketplace 106 over time. Other potential trading partners may thenreference the reputation for purposes of assessing credibility andtrustworthiness.

Navigation of the network-based marketplace 106 may be facilitated by anavigation engine 1212. For example, a browse module (not shown) of thenavigation engine 1212 allows users to browse various category, catalog,or inventory data structures according to which listings may beclassified within the publication system 1200. Various other navigationapplications within the navigation engine 1212 may be provided tosupplement the browsing applications. For example, the navigation engine1212 may include the communication module 116, as previously described.

In order to make listings available via the networked system 1102 asvisually informing and attractive as possible, the publication system1200 may include an imaging engine 1214 that enables users to uploadimages for inclusion within publications and to incorporate imageswithin viewed listings. The imaging engine 1214 may also receive imagedata from a user as a search query and utilize the image data toidentify an item depicted or described by the image data.

A listing creation engine 1216 allows users (e.g., buyers) toconveniently author listings of items. In one embodiment, the listingspertain to goods or services that a user (e.g., a buyer) wishes totransact via the publication system 1200. In other embodiments, a usermay create a listing that is an advertisement or other form ofpublication.

A listing management engine 1218 allows the users to manage suchlistings. Specifically, where a particular user has authored orpublished a large number of listings, the management of such listingsmay present a challenge. The listing management engine 1218 provides anumber of features (e.g., auto-relisting, inventory level monitors,etc.) to assist the user in managing such listings. The listingmanagement engine 1218 may include the listing module 118, as previouslydescribed.

A post-listing management engine 1220 also assists users with a numberof activities that typically occur post-listing. For example, uponcompletion of a transaction facilitated by the one or more auctionengines 1204, a buyer may wish to leave feedback regarding a particularseller. To this end, the post-listing management engine 1220 provides aninterface to the reputation engine 1210 allowing the buyer toconveniently provide feedback regarding multiple sellers to thereputation engine 1210. Another post-listing action may be shipping ofsold items whereby the post-listing management engine 1220 may assist inprinting shipping labels, estimating shipping costs, and suggestingshipping carriers.

A search engine 1222 performs searches for publications in the networkedsystem 1102 that match a query. In example embodiments, the searchengine 1222 comprises a search module (not shown) that enables keywordsearches of publications published via the publication system 1200.Further, for example, the search engine 1222 may perform the functionspreviously described in reference to the search engine 121. In a furtherembodiment, the search engine 1222 may take an image received by theimaging engine 1214 as an input for conducting a search. The searchengine 1222 takes the query input and determines a plurality of matchesfrom the networked system 1102 (e.g., publications stored in thedatabase 1126). It is noted that the functions of the search engine 1222may be combined with the navigation engine 1212. The search engine 1222,in the publication system 1200, may perform the functionality previouslydescribed with respect to the search engine 121.

A user activity detection engine 1224 in FIG. 12 may monitor useractivity during user sessions and detect a change in the level of useractivity that, as discussed in more detail below, may predict that auser is about to make a purchase. The exact amount of change in thelevel of user activity may vary. A general guideline may be to monitoracross multiple sessions and detect any significant increase over time(for example the activity level doubling or tripling in a short span).In one embodiment, when the user activity detection engine 1224 detectssuch a condition, the ecommerce system may make an intervention toprovide content for display to the user in an effort to improve theprobability that the user will make a purchase, and/or also to motivethe user to make the purchase on the ecommerce system site instead ofmoving to a competitor site in search of a better purchase. Statedanother way, activity over time and at different times before a purchaseaction provides an opportunity to personalize marketing to a user, basedon time, by intervention as discussed above. Additional examples ofincluding a temporal frame in that marketing personalization arediscussed below. The publication system 1200 may further include thepopularity module 128, as previously described.

Although the various components of the publication system 1200 have beendefined in terms of a variety of individual modules and engines, askilled artisan will recognize that many of the items can be combined ororganized in other ways and that not all modules or engines need to bepresent or implemented in accordance with example embodiments.Furthermore, not all components of the publication system 1200 have beenincluded in FIG. 12. In general, components, protocols, structures, andtechniques not directly related to functions of exemplary embodiments(e.g., dispute resolution engine, loyalty promotion engine,personalization engines) have not been shown or discussed in detail. Thedescription given herein simply provides a variety of exemplaryembodiments to aid the reader in an understanding of the systems andmethods used herein.

Data Structures

FIG. 13 is a high-level entity-relationship diagram, illustratingvarious tables 1250 that may be maintained within the databases 1126 ofFIG. 11, and that are utilized by and support the publication system1200 and payment system 1122, both of FIG. 11. A user table 1252 maycontain a record for each of the registered users of the networkedsystem 1102 (e.g., network-based marketplace 106) of FIG. 11 and FIG. 5.A user may operate as a seller, a buyer, or both, within thenetwork-based marketplace 106 (e.g., FIG. 11 and FIG. 5). In one exampleembodiment, a buyer may be a user that has accumulated value (e.g.,commercial or proprietary currency), and is accordingly able to exchangethe accumulated value for items that are offered for sale by thenetwork-based marketplace 106.

The tables 1250 may also include an items table 1254 (e.g., items table142) in which item records (e.g., listings) are maintained for goods andservices (e.g., items) that are available to be, or have been,transacted via the network-based marketplace 106. Item records (e.g.,listings) within the items table 1254 may furthermore be linked to oneor more user records within the user table 1252, so as to associate aseller and one or more actual or potential buyers with an item record(e.g., listing).

A transaction table 1256 may contain a record for each transaction(e.g., a purchase or sale transaction or auction) pertaining to itemsfor which records exist within the items table 1254.

An order table 1258 may be populated with order records, with each orderrecord being associated with an order. Each order, in turn, may beassociated with one or more transactions for which records exist withinthe transaction table 1256.

Bid records within a bids table 1260 may relate to a bid received at thenetwork-based marketplace 106 in connection with an auction-formatlisting supported by the auction engine(s) 1204 of FIG. 12. A feedbacktable 1262 may be utilized by one or more reputation engines 1210 ofFIG. 12, in one example embodiment, to construct and maintain reputationinformation concerning users in the form of a feedback score. A historytable 1264 may maintain a history of transactions to which a user hasbeen a party. One or more attributes tables 1266 may record attributeinformation that pertains to items for which records exist within theitems table 1254. Considering only a single example of such anattribute, the attributes tables 1266 may indicate a currency attributeassociated with a particular item, with the currency attributeidentifying the currency of a price for the relevant item as specifiedby a seller. A search table 1268 may store search information that hasbeen entered by a user (e.g., a buyer) who is looking for a specifictype of listing. The tables 1250 may include the popularity table 144and the search metadata 140, both as previously described.

Machine

FIG. 15 is a block diagram illustrating components of a machine 1300,according to some example embodiments, able to read instructions 1324from a machine-readable medium 1322 (e.g., a non-transitorymachine-readable medium, a machine-readable storage medium, acomputer-readable storage medium, or any suitable combination thereof)and perform any one or more of the methodologies discussed herein, inwhole or in part. Specifically, FIG. 15 shows the machine 1300 in theexample form of a computer system (e.g., a computer) within which theinstructions 1324 (e.g., software, a program, an application, an applet,an app, or other executable code) for causing the machine 1300 toperform any one or more of the methodologies discussed herein may beexecuted, in whole or in part.

In alternative embodiments, the machine 1300 operates as a standalonedevice or may be connected (e.g., networked) to other machines. In anetworked deployment, the machine 1300 may operate in the capacity of aserver machine or a client machine in a server-client networkenvironment, or as a peer machine in a distributed (e.g., peer-to-peer)network environment. The machine 1300 may be a server computer, a clientcomputer, a personal computer (PC), a tablet computer, a laptopcomputer, a netbook, a cellular telephone, a smartphone, a set-top box(STB), a personal digital assistant (PDA), a web appliance, a networkrouter, a network switch, a network bridge, or any machine capable ofexecuting the instructions 1324, sequentially or otherwise, that specifyactions to be taken by that machine. Further, while only a singlemachine is illustrated, the term “machine” shall also be taken toinclude any collection of machines that individually or jointly executethe instructions 1324 to perform all or part of any one or more of themethodologies discussed herein.

The machine 1300 includes a processor 1302 (e.g., a central processingunit (CPU), a graphics processing unit (GPU), a digital signal processor(DSP), an application specific integrated circuit (ASIC), aradio-frequency integrated circuit (RFIC), or any suitable combinationthereof), a main memory 1304, and a static memory 1306, which areconfigured to communicate with each other via a bus 1308. The processor1302 may contain microcircuits that are configurable, temporarily orpermanently, by some or all of the instructions 1324 such that theprocessor 1302 is configurable to perform any one or more of themethodologies described herein, in whole or in part. For example, a setof one or more microcircuits of the processor 1302 may be configurableto execute one or more modules (e.g., software modules) describedherein.

The machine 1300 may further include a graphics display 1310 (e.g., aplasma display panel (PDP), a light emitting diode (LED) display, aliquid crystal display (LCD), a projector, a cathode ray tube (CRT), orany other display capable of displaying graphics or video). The machine1300 may also include an alphanumeric input device 1312 (e.g., akeyboard or keypad), a cursor control device 1314 (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, an eye trackingdevice, or other pointing instrument), a storage unit 1316, an audiogeneration device 1318 (e.g., a sound card, an amplifier, a speaker, aheadphone jack, or any suitable combination thereof), and a networkinterface device 1320.

The storage unit 1316 includes the machine-readable medium 1322 (e.g., atangible and non-transitory machine-readable storage medium) on whichare stored the instructions 1324 embodying any one or more of themethodologies or functions described herein. The instructions 1324 mayalso reside, completely or at least partially, within the main memory1304, within the processor 1302 (e.g., within the processor's cachememory), or both, before or during execution thereof by the machine1300. Accordingly, the main memory 1304 and the processor 1302 may beconsidered machine-readable media (e.g., tangible and non-transitorymachine-readable media). The instructions 1324 may be transmitted orreceived over the network 1390 via the network interface device 1320.For example, the network interface device 1320 may communicate theinstructions 1324 using any one or more transfer protocols (e.g.,hypertext transfer protocol (HTTP)).

In some example embodiments, the machine 1300 may be a portablecomputing device, such as a smart phone or tablet computer, and have oneor more additional input components 1330 (e.g., sensors or gauges).Examples of such input components 1330 include an image input component(e.g., one or more cameras), an audio input component (e.g., amicrophone), a direction input component (e.g., a compass), a locationinput component (e.g., a global positioning system (GPS) receiver), anorientation component (e.g., a gyroscope), a motion detection component(e.g., one or more accelerometers), an altitude detection component(e.g., an altimeter), and a gas detection component (e.g., a gassensor). Inputs harvested by any one or more of these input components1330 may be accessible and available for use by any of the modulesdescribed herein.

As used herein, the term “memory” refers to a machine-readable mediumable to store data temporarily or permanently and may be taken toinclude, but not be limited to, random-access memory (RAM), read-onlymemory (ROM), buffer memory, flash memory, and cache memory. While themachine-readable medium 1322 is shown in an example embodiment to be asingle medium, the term “machine-readable medium” should be taken toinclude a single medium or multiple media (e.g., a centralized ordistributed database, or associated caches and servers) able to storeinstructions 1324. The term “machine-readable medium” shall also betaken to include any medium, or combination of multiple media, that iscapable of storing the instructions 1324 for execution by the machine1300, such that the instructions 1324, when executed by one or moreprocessors of the machine 1300 (e.g., processor 1302), cause the machine1300 to perform any one or more of the methodologies described herein,in whole or in part. Accordingly, a “machine-readable medium” refers toa single storage apparatus or device, as well as cloud-based storagesystems or storage networks that include multiple storage apparatus ordevices. The term “machine-readable medium” shall accordingly be takento include, but not be limited to, one or more tangible (e.g.,non-transitory) data repositories in the form of a solid-state memory,an optical medium, a magnetic medium, or any suitable combinationthereof.

Furthermore, the machine-readable medium is non-transitory in that itdoes not embody a propagating signal. However, labeling the tangiblemachine-readable medium as “non-transitory” should not be construed tomean that the medium is incapable of movement—the medium should beconsidered as being transportable from one physical location to another.Additionally, since the machine-readable medium is tangible, the mediummay be considered to be a machine-readable device.

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Certain embodiments are described herein as including logic or a numberof components, modules, or mechanisms. Modules may constitute softwaremodules (e.g., code stored or otherwise embodied on a machine-readablemedium or in a transmission medium), hardware modules, or any suitablecombination thereof. A “hardware module” is a tangible (e.g.,non-transitory) unit capable of performing certain operations and may beconfigured or arranged in a certain physical manner. In various exampleembodiments, one or more computer systems (e.g., a standalone computersystem, a client computer system, or a server computer system) or one ormore hardware modules of a computer system (e.g., a processor or a groupof processors) may be configured by software (e.g., an application orapplication portion) as a hardware module that operates to performcertain operations as described herein.

In some embodiments, a hardware module may be implemented mechanically,electronically, or any suitable combination thereof. For example, ahardware module may include dedicated circuitry or logic that ispermanently configured to perform certain operations. For example, ahardware module may be a special-purpose processor, such as a fieldprogrammable gate array (FPGA) or an ASIC. A hardware module may alsoinclude programmable logic or circuitry that is temporarily configuredby software to perform certain operations. For example, a hardwaremodule may include software encompassed within a general-purposeprocessor or other programmable processor. It will be appreciated thatthe decision to implement a hardware module mechanically, in dedicatedand permanently configured circuitry, or in temporarily configuredcircuitry (e.g., configured by software) may be driven by cost and timeconsiderations.

Accordingly, the phrase “hardware module” should be understood toencompass a tangible entity, and such a tangible entity may bephysically constructed, permanently configured (e.g., hardwired), ortemporarily configured (e.g., programmed) to operate in a certain manneror to perform certain operations described herein. As used herein,“hardware-implemented module” refers to a hardware module. Consideringembodiments in which hardware modules are temporarily configured (e.g.,programmed), each of the hardware modules need not be configured orinstantiated at any one instance in time. For example, where a hardwaremodule comprises a general-purpose processor configured by software tobecome a special-purpose processor, the general-purpose processor may beconfigured as respectively different special-purpose processors (e.g.,comprising different hardware modules) at different times. Software(e.g., a software module) may accordingly configure one or moreprocessors, for example, to constitute a particular hardware module atone instance of time and to constitute a different hardware module at adifferent instance of time.

Hardware modules can provide information to, and receive informationfrom, other hardware modules. Accordingly, the described hardwaremodules may be regarded as being communicatively coupled. Where multiplehardware modules exist contemporaneously, communications may be achievedthrough signal transmission (e.g., over appropriate circuits and buses)between or among two or more of the hardware modules. In embodiments inwhich multiple hardware modules are configured or instantiated atdifferent times, communications between such hardware modules may beachieved, for example, through the storage and retrieval of informationin memory structures to which the multiple hardware modules have access.For example, one hardware module may perform an operation and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware module may then, at a latertime, access the memory device to retrieve and process the storedoutput. Hardware modules may also initiate communications with input oroutput devices, and can operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions describedherein. As used herein, “processor-implemented module” refers to ahardware module implemented using one or more processors.

Similarly, the methods described herein may be at least partiallyprocessor-implemented, a processor being an example of hardware. Forexample, at least some of the operations of a method may be performed byone or more processors or processor-implemented modules. As used herein,“processor-implemented module” refers to a hardware module in which thehardware includes one or more processors. Moreover, the one or moreprocessors may also operate to support performance of the relevantoperations in a “cloud computing” environment or as a “software as aservice” (SaaS). For example, at least some of the operations may beperformed by a group of computers (as examples of machines includingprocessors), with these operations being accessible via a network (e.g.,the Internet) and via one or more appropriate interfaces (e.g., anapplication program interface (API)).

The performance of certain operations may be distributed among the oneor more processors, not only residing within a single machine, butdeployed across a number of machines. In some example embodiments, theone or more processors or processor-implemented modules may be locatedin a single geographic location (e.g., within a home environment, anoffice environment, or a server farm). In other example embodiments, theone or more processors or processor-implemented modules may bedistributed across a number of geographic locations.

Some portions of the subject matter discussed herein may be presented interms of algorithms or symbolic representations of operations on datastored as bits or binary digital signals within a machine memory (e.g.,a computer memory). Such algorithms or symbolic representations areexamples of techniques used by those of ordinary skill in the dataprocessing arts to convey the substance of their work to others skilledin the art. As used herein, an “algorithm” is a self-consistent sequenceof operations or similar processing leading to a desired result. In thiscontext, algorithms and operations involve physical manipulation ofphysical quantities. Typically, but not necessarily, such quantities maytake the form of electrical, magnetic, or optical signals capable ofbeing stored, accessed, transferred, combined, compared, or otherwisemanipulated by a machine. It is convenient at times, principally forreasons of common usage, to refer to such signals using words such as“data,” “content,” “bits,” “values,” “elements,” “symbols,”“characters,” “terms,” “numbers,” “numerals,” or the like. These words,however, are merely convenient labels and are to be associated withappropriate physical quantities.

Unless specifically stated otherwise, discussions herein using wordssuch as “processing,” “computing,” “calculating,” “determining,”“presenting,” “displaying,” or the like may refer to actions orprocesses of a machine (e.g., a computer) that manipulates or transformsdata represented as physical (e.g., electronic, magnetic, or optical)quantities within one or more memories (e.g., volatile memory,non-volatile memory, or any suitable combination thereof), registers, orother machine components that receive, store, transmit, or displayinformation. Furthermore, unless specifically stated otherwise, theterms “a” or “an” are herein used, as is common in patent documents, toinclude one or more than one instance. Finally, as used herein, theconjunction “or” refers to a non-exclusive “or,” unless specificallystated otherwise.

We claim:
 1. A system comprising: a seed module, implemented using oneor more processors, that is configured to identify a first listing as aseed listing, the first listing being included in a first plurality oflistings that respectively describe items being offered for sale on anetwork-based marketplace; a filter name module, implemented using oneor more processors, that is configured to identify a seed filter contextbased on the seed listing and probabilities, the seed filter contextincluding a plurality of attribute-value pairs including a firstattribute-value pair and a second attribute-value pair, the firstattribute-value pair being a first filter and a second attribute-valuepair being a second filter, the probabilities describing occurrences ofattribute-value pairs in a plurality of listings that respectivelydescribe items that were previously transacted on the network-basedmarketplace; the seed module is further configured to: extract valuesfrom the seed listing based on the seed filter context, initialize theseed filter context based on the values extracted from the seed listing,generate search results based on the seed filter context, the searchresults including a second plurality of listings that are identifiedfrom the first plurality of listings, and communicate interfaceinformation, over a network, to a client machine, the interfaceinformation including the seed filter context and at least one listingfrom the second plurality of listings.
 2. The system of claim 1, whereinthe filter name module is to identify the seed filter context based on acategory included in the seed listing that is utilized for presentationof the seed listing in a hierarchical category structure on thenetwork-based marketplace.
 3. The system of claim 1, wherein a number offilters in the seed filter context is limited by a predeterminedparameter.
 4. The system of claim 1, wherein the first attribute-valuepair includes a first attribute and a first value, and wherein thesecond attribute-value pair includes a second attribute and a secondvalue
 5. The system of claim 4, wherein the first and second values areinitialized with values that are extracted from the seed listing.
 6. Thesystem of claim 1, wherein the filter name module is further configuredto identify a seed filter proposal including a second plurality offilters, wherein the filter name module is further configured toidentify the filter proposal is based on the seed filter context andprobabilities that describe occurrences of attribute-value pairs in thefirst plurality of listings that respectively describe items that werepreviously transacted on a network-based marketplace.
 7. The system ofclaim 1, wherein the first plurality of listings includes a listing thatincludes a title, wherein the identifying the second plurality offilters includes sorting the second plurality of filters based on aprobability of the first attribute-value pair randomly occurring in thetitle.
 8. The system of claim 1, wherein: the seed module is furtherconfigured to identify search results based on the seed filter context,the candidate search results include one or more candidate listings, andthe seed module is further configured to sort the search results basedon an item similarity ranking in accordance with the seed listing togenerate the search results, wherein the item similarity ranking is aranking of the one or more candidate listings from the most to leastsimilar to the seed listing.
 9. The system of claim 8, wherein the seedmodule is further configured to: select a portion of the search resultsthat is most similar to the seed listing, and sort the portion of thecandidate search results according to a criterion that is selected by auser to generate the search results.
 10. A method comprising:identifying a first listing as a seed listing, the first listing beingincluded in a first plurality of listings that respectively describeitems being offered for sale on a network-based marketplace, theidentifying being executed by at least one processor; identifying a seedfilter context based on the seed listing and probabilities, the seedfilter context including a plurality of attribute-value pairs includinga first attribute-value pair and a second attribute-value pair, thefirst attribute-value pair being a first filter and a secondattribute-value pair being a second filter, the probabilities describingoccurrences of attribute-value pairs in a plurality of listings thatrespectively describe items that were previously transacted on thenetwork-based marketplace; extracting values from the seed listing basedon the seed filter context; initializing the seed filter context basedon the values extracted from the seed listing; generating search resultsbased on the seed filter context, the search results including a secondplurality of listings that are identified from the first plurality oflistings; and communicating interface information, over a network, to aclient machine, the interface information including the seed filtercontext and at least one listing from the second plurality of listings.11. The method of claim 10, wherein the identifying the seed filtercontext is based on a category included in the seed listing that isutilized for presentation of the seed listing in a hierarchical categorystructure on the network-based marketplace.
 12. The method of claim 10,wherein a number of filters in the seed filter context is limited by apredetermined parameter.
 13. The method of claim 10, wherein the firstattribute-value pair includes a first attribute and a first value, andwherein the second attribute-value pair includes a second attribute anda second value
 14. The method of claim 13, wherein the first and secondvalues are initialized with values that are extracted from the seedlisting.
 15. The method of claim 10, further comprising identifying aseed filter proposal including a second plurality of filters, theidentifying the filter proposal is based on the seed filter context andprobabilities describing occurrences of attribute-value pairs in thefirst plurality of listings that respectively describe items that werepreviously transacted on a network-based marketplace.
 16. The method ofclaim 10, wherein the first plurality of listings includes a listingthat includes a title, wherein the identifying the second plurality offilters includes sorting the second plurality of filters based on aprobability of the first attribute-value pair randomly occurring in thetitle.
 17. The method of claim 10, further comprising: identifyingcandidate search results based on the seed filter context, wherein thecandidate search results include one or more candidate listings; andsorting the candidate search results based on an item similarity rankingin accordance with the seed listing to generate the search results,wherein the item similarity ranking is a ranking of the one or morecandidate listings from the most to least similar to the seed listing.18. The method of claim 17, further comprising: selecting a portion ofthe search results that is most similar to the seed listing; and sortingthe portion of the candidate search results according to a criteria thatis selected by a user to generate the search results.
 19. Amachine-readable hardware storage devices storing a set of instructionsthat, when executed by a processor of a machine, cause the machine toperform operations comprising: identifying a first listing as a seedlisting, the first listing being included in a first plurality oflistings that respectively describe items being offered for sale on anetwork-based marketplace; identifying a seed filter context based onthe seed listing and probabilities, the seed filter context including aplurality of attribute-value pairs including a first attribute-valuepair and a second attribute-value pair, the first attribute-value pairbeing a first filter and a second attribute-value pair being a secondfilter, the probabilities describing occurrences of attribute-valuepairs in a plurality of listings that respectively describe items thatwere previously transacted on the network-based marketplace; extractingvalues from the seed listing based on the seed filter context;initializing the seed filter context based on the values extracted fromthe seed listing; generating search results based on the seed filtercontext, the search results including a second plurality of listingsthat are identified from the first plurality of listings; andcommunicating interface information, over a network, to a clientmachine, the interface information including the seed filter context andat least one listing from the second plurality of listings.
 20. Amachine-readable hardware storage devices of claim 19, wherein the firstplurality of listings includes a listing that includes a title, whereinthe identifying the second plurality of filters includes sorting thesecond plurality of filters based on a probability of the firstattribute-value pair randomly occurring in the title.